The Role of Music in Films Essay

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Literature review, methodology, findings and analysis, similarities in the two compositions.


The main objective of the research thesis will be analysis of distinctive music based on the concepts of tone, composition, and literary styles. Also, the paper will identify sound effects such as ambient sound within the musical score incorporated to complete other effects such as digenetic sounds dependent on sight upon hearing within different cultures. The objective captures the musical composition by Philip Glass in “ The Hours ” film and Anton Karas in “ The Third Man ” film on the significance of these compositions and distinguish unique characteristics in each. Besides, the treatise identifies literary styles such as repetition, rhyme, and esthetic functionality to create right mood and support various themes narrated in motion picture films.

Music expression in motion picture films assumes different models which often operate simultaneously. Overtime, various authors have investigated these models. Interestingly, these models share same components on the basis of listener, performer, and composer. This part elaborates and comprehensively expands the understanding of significance of musical composition and soundtracks in motion picture films to create an elucidated and in-depth interrelationships existing between image motion and music in the background.

Anton Karas’ composition in the film “ The Third Man ”

Anton Karas performed the score in the film “ The Third Man” . This composition include zither instrument sound common in the late 1940s and early 1950s. In order to depict the mood of the post Second World War in Vienna, the narrative to the film “ The Third Man” demanded appropriate music away from the usual heavy schmaltzy mixed with ‘orchestral waltzes’.

Since Anton Karas music consists of fascinating jangling music of melancholy nature, Reed adopted this kind of music for the score of the film “ The Third Man”. Also referred to as “The Harry Lime Theme”, this instrumental composition by Anton formed the sole soundtrack for the entire film. Zither composition appreciated culture of Vienna at the time of its release. Reflectively, this composition was a melody modification from a practice book. Released as a single, this composition had the “Harry Lime theme” on side A, and “Café Mozart Waltz” on side B 1 .

Philip Glass composition in the film “ The Hours”

An American born composer, Philip Glass is recognized for his contribution in classical music composition in the 20 th century. Philip’s composition is controversially referred to as minimalist. As a soundtrack composer in the film “ The Hours” , Philip presents himself as an author of repetitive structured music which has stylistically evolved over time. The background music for the film “ The Hours” presents a prolific composition which ensembles symphonies, chamber music, and operas 2 .

This romantic and lyrical style played out in the film score for the motion picture film “ The Hour” . Evocated in thematically instigated improvisatory chords, sound tracks for this film demonstrate chamber textured orchestral classic technique of rigorously and precise popular excursions in modern composition 3 . In addition, these soundtracks are inclusive of a fusion of electronic music, ambient music, and world music.

In order to present a clear understanding in of significance of music composition by Philip Glass in “ The Hours ” and Anton Karas in “ The third Man “, it is essential to review the unique feature of each composition and relate them to the themes, that is, qualitative and quantitative analysis of symbolic ‘tonation’, and movement as part of the desired outcome in terms of perception by the audience. Music composition is more than just the sound especially in movies. It reflects the mood of the displayed motion pictures and explains themes through sound.

Through physical observation of the motion pictures in each of the films and the soundtrack in the background for every scene, it is important to interpret the significance of each composition by use of literary analysis tools such as observation of symbolism, ‘tonation’, mood of each episode, the character traits of each character, and other compositions by the same authors. Notwithstanding, the methodology includes analysis of “The Harry Lime Theme”, an instrumental composition by Anton which formed the sole soundtrack for the entire film called ‘Zither composition’ and this style appreciated culture of Vienna at that time after the Second World War 4 .

Analysis of role of music in the film “The Third Man”

The film “ The Third Man ” is often described as a classical masterpiece of the early 1950s due to its unique theme and musical composition by Anton Karas 5 . The unique musical composition score, cinematography, and performances have perfectly fused the narrative by Graham Greene and artistic music by Anton using zither only. Across the film, zither music has expressed the main theme especially when the characters interact depending on emotion and the mood in every scene. Therefore, this part reflects on role of artistic composition by Anton in the scenes across the movie.

The type of music used as soundtrack for the movie “ The Third Man” is a composition of Zither. A single but frothy composition, this kind of music best suits the culture of Vienna at the time of its release, as it symbolizes evocation purpose especially after the aftermath of the Second World War. To align to the traditional setting aspects on production design and the society, this composition heightens hyper-real palate of emotional expression 6 . Moreover, this choice of music was essential in the need for a proper balance of production aural, sound accordion, and permeates scenes. Subsequently, this balance has facilitated the addition of decadent love feeling across the film and in the cast. Therefore, the choice of instrument used and monotony of the soundtrack in the film “ The Third Man” to create the unique coded sounds for recognizable geographical access 7 .

In line with the nature of structures at the time of its release; bombed buildings, decaying facades, and cracked streets, the sound of the background music from zither creates an enigmatic visual production laced with an in-depth feeling of historical dark moment expressed via creation of a sound production that is laced with instrumental creation of dread. To dramatize this state, the score was created by Anton and directed by Carol Reed.

In addition, this composition presents the instinct of two extreme situations to the viewers. As viewers absorb the acts in oppressive scenes, Anton Karas’ music composition creeps in the background of the motion pictures and create a caricature in the minds of the audience as a reminder of the lost in the just concluded war such as destroyed buildings, cracked streets, and poor lighting. Besides, this juxtaposition startles with its hallmark score of single instrumentation to create successful film music for the movie “ The Third Man”.

Throughout the film, Karas’ musical composition has helped to fine tune the two main themes of “The Harry Lime Theme” and “The Café Mozart Waltz Theme”. Musically, this presentation is but a simple expression of various themes occurring in a series of events. Intrinsically, on the facets of nostalgia, these variations speak tones of dormant and tonic expression to create a sentimental charm carried outside the frame of viewing. Besides the “Harry Theme” and “The Café Mozart Waltz” theme create a lilting melody of more ironic nature especially with the arrangement of dormant and tonic expressions aligned to perfectly fit in the context 8 .

The sound in this composition is uniform, with rich, deep bass strings, proper ‘tonation’, and upper register brightness. Besides, the performance of this masterpiece composition on zither instrument creates a feeling of sensitivity and general panache. According to Richard Wagner, proper use of musical instrument in film soundtrack creates a feeling of complete uniformity as it expresses the hidden components which cannot be explained by mere words 9 . Therefore, to create a comfortable impact, the composition of this film is solely from Anton who perfected the balance between consummated symbolism and cinematic images. Anton, in his creation, used catchy and ‘twangy’ soundtrack music for the background of the film to work out a match between the displayed images and emotions.

Analysis of the role of music in the film “ The Hours ”

Philip Glass musical composition for the film “The Hours” is often described as a comprehensive repetitive art since it contain numerous emotive haunting, and lyrical music produced by Glass. This composition perfectly and superbly complements the theme of the movie and expresses different emotions as displayed by motion pictures. Philip’s composition in the movie “ The Hours” has transformed this motion picture film into a powerful drama.

In the film background, Glass uses repetition for emphasis of emotional balance and mood across the shots or scenes 10 . This kind of musical composition is unique in the sense that it uses cycle system most appropriate for the theme of the film “ The Hours”. In the film, Stephen Daldry’s narrative is a collection of script representing three different women who live in different dimensions of time, but are represented as coexisting simultaneously in one film.

Music is symbolic of the invisible aspects of emotive and themes in a piece of literature such as motion picture film. Across the film, Glass’ music is consistent though repetitive and lavish especially on the facets of non-separately rhymes and swirling music. In order to fully understand the kind of music use in the film, it is of essence to reflect on repetition, the magnitude of the same, rhymes and rhythm, and their connection to what is displayed in the screen 11 .

As a matter of fact, Glass’ musical composition in the film is described as a score which defines synopsis of the storyline by application of reacquiring esthetic, dramatic, visionary, and pleasing soundtrack which appeals to the audience. Besides, this masterpiece has been used by the director to lightens up intense scenes and provide a compact connection between different scenes occurring concurrently. In line with Glass’ composition, the repetitive music reflects what is displayed in the screen.

In the process of creating ambience and affirming the plot, musical composition in the film “ The Hours” is the most appropriate for the film. The features such as repetition and rhyme creates a lasting impact that artistically facilitates the evocation of emotions of the cast of characters, while at the same time, making the viewers develop same notion and feeling. In instances where repetition is frequent, the audience is in a position to predict the mood and prepare for an eerie infatuation of tension or joy 12 .

Without Glass’ composition, the film would be incomplete and almost impossible to understand especially on the periphery of emotional display. Interestingly, this kind of music evokes sensation and relates situational occurrences across the shots. For instance, a slow dragging composition would inform the audience of sadness, and a light song resonate a relaxed mood the character feels. Therefore, a happy composition lightens the perceived mood in the film 13 .

Stephen Daldry’s film “ The Hour” cannot function properly without Glass’ composition as its soundtrack. Despite the different time setting for the three women balanced simultaneously in the film, Glass’ music bridges the gap of variance in time through repetition. The main actor, Virginia Woolf displays minute kindness acts, randomly swinging moods, deviation from normalcy. These tendencies are artistically brought out in the film through use of music to depict in different emotions.

In the words of Virginia Woolf, “the question of things happening, normally, all the time” 14 is vital in selecting the right composition for a film soundtrack. The nonesuch composition by Glass comes in handy to solve the puzzle. The abrasive mixture of harmonies in this composition signifies loathing uncertainty, and faster rhythm signifies pursuit, and sustained high pitch is symbolic of prosthetic horror heralded to ensure that the audience gives the film undistracted attention 15 .

This music signifies emotions besides suggesting the same in the film. As an aspect of narrative cueing, this composition has referential significance as it reflects on formal demarcation, establishing character traits and geographical setting as an indication of connotative intercepts and events occurring simultaneously as a illustrated by motion pictures. In addition, Glass’ music offers continuity to the film by use of repetition. Through rhythmic continuity and formal shots, this aspect creates a consistent and in-depth transition from a scene to another as it fills the gaps found between two scenes. Glass states that “writing music is listening to music; you don’t have to imagine it, it’s already there” 16 . Through series of consistent variation and repetition in the instrumentation and musical material, this musical composition facilitates contraction of relevant and formal unity in the story line and synopsis.

Glass’ music conveys the thoughts, feelings, and internal life of characters in the film “ The Hours”. In the process of augmenting expressed but unspoken implications and thoughts, this composition creates an interesting tool for understanding the underlying drama and psychological refinements. Through “The Hours” score, each character is conveyed and associated with a composition theme, that is, the audience is able to associate a particular sound to a character or character trait of that character.

Without Glass’ composition, this aspect would remain void and various character traits and significance of a character would not be fully realized or altogether cease to function symbolically 17 . A cross the shots, use of music to personify a character are consistent and communicate coherent ideas which are defined clearly to maintain the created identity especially when the same has been modified in the previous appearance. In the process, this aspect of music purpose to symbolically represent a trait, idea, mind state, and place 18 .

Reflectively, the music used in these two films coalesces on the same symbolic meanings to create a sense of sequential order. On this facet, these compositions are intrinsic of same sound movements. Though these compositions are simple in creation, they represent a recurrent theme characterized by emphasis on the beginning and ending. Moreover, these compositions assign connotation to align ambiguity to memory and visualization of scenes.

Conclusively, Anton and Glass have succeeded in incorporating their composition as soundtrack for the films “ The Third Man” and “ The Hours” respectively. Accented on attentive structure alignment, focusing devise in understanding the role of musical composition in a film should be inclusive of a continuum of relevant score as component of digenesis which allows the viewers to make prior judgment and remain attentive as the film rolls. Generally, from the stimuli created by movie excerpts and series of dynamic structural alignment, Anton and Glass’ music have played dominant role as associational judgment shifts from accent structure to receding support role in these films.

Boltz, Marilyn. “Musical Soundtracks as a schematic influence on the cognitive processing of filmed events”. Musical Perception 18, no. 4 (2001): 427-454.

Cohen, Annabel. “Music as a source of emotion in film. ” In Music and Emotions: Theory and Research , edited by Patrik Juslin, 21-34. New York: Oxford University Press, 2001.

Grenade, Anthony. The Hour. DVD. Directed by Daldry Stephen. 2002. Los Angeles, LA: Sound trackers, 2002.

Soderbergh, Steven, and Gilroy Thomas. Commentary of The Third Man . IMDB, 2007. CD.

Zingale, John. The Third Man . DVD. Directed by Carol Reed. 1949. London, UK: Liverpool University, 1951.

  • Anthony Grenade, The Hour, DVD, directed by Daldry Stephen (2002; Los Angeles, LA: Sound trackers, 2002.), DVD.
  • Grenade, The Hour
  • Annabel Cohen. “Music as a source of emotion in film ” , in Music and Emotions: Theory and Research , ed. Patrik Juslin (New York: Oxford University Press, 2001), 31.
  • Cohen. “Music as a source of emotion in film , 37.
  • Steven Soderbergh, and Gilroy Thomas. Commentary of The Third Man (IMDB, 2007.), CD.
  • Marilyn Boltz, “Musical Soundtracks as a schematic influence on the cognitive processing of filmed events,” Musical Perception 18, no. 4 (2001): 432.
  • John Zingale. The Third Man , DVD, directed by Carol Reed (1949. London, UK: Liverpool University, 1951.), DVD.
  • Annabel Cohen. “Music as a source of emotion in film ” , in Music and Emotions: Theory and Research , ed. Patrik Juslin (New York: Oxford University Press, 2001), 39.
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IvyPanda. (2020, June 17). The Role of Music in Films.

"The Role of Music in Films." IvyPanda , 17 June 2020,

IvyPanda . (2020) 'The Role of Music in Films'. 17 June.

IvyPanda . 2020. "The Role of Music in Films." June 17, 2020.

1. IvyPanda . "The Role of Music in Films." June 17, 2020.

IvyPanda . "The Role of Music in Films." June 17, 2020.

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Film Music: A Very Short Introduction (1st edn)

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1 What does film music do?

  • Published: May 2010
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Music in film achieves a number of things: it establishes setting; it creates atmosphere; it calls attention to elements; it reinforces or foreshadows narrative developments; it gives meaning to a character's actions or translates their thoughts; and it creates emotion. ‘What does film music do?’ shows, using the example of ‘Stuck in the Middle With You’ from Pulp Fiction , how music adds an extra dimension to a film, uniting a series of images and encouraging audience absorption into the story. Music in Hollywood operates differently from that in other world cinemas such as Hindi, Brazilian, or Bengali. National and cultural traditions create distinct practices of film music.

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The Evolution of Music in Film and its Psychological Impact on Audiences

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The purpose here is not to trace the history of music in film, but to survey the literature about music in film written between 1980 and 1996. Film music literature is a strange hybrid, much like its subject, existing in a limbo cut off from the main body of both its progenitors: neither film studies nor musicology have paid much attention to film music in the past, and like a squalling brat, film music studies have continued to protest loudly the neglect. While it is true that film studies and musicology have been largely uninterested in film music—or, to be fair, ill-equipped theoretically to approach the synthetic form—the vociferous plaints of complete neglect are gradually being exposed as hyperbole.

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We all know that music scores play an important role in movies. But which specific functions does music fulfil with respect to the other film elements? And how does it manage to communicate emotions and to help shape the global meaning of a cinematic experience? In Part 2 of this Interactive workshop we will explore the functions that music plays in movies. Part 1: The interaction between image and sound is also available at

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This study focuses on an example of Cahit Berkay who has an important place in the context of film music and the importance, effect and functions of music in the Turkish Cinema. Essentially of the study within the frame of literature review, music, cinema, the relation between music and the field of arts, Turkish Cinema and music, also provides information relates with Cahit Berkay in the context of music in Turkish cinema. The research part of the study, to measure the effects of the soundtracks composed by Cahit Berkay, “Selvi Boylum ve Al Yazmalım” and “Çöpçüler Kralı” movies detected by sample for purpose, versions separated from music with technical method and original music versions interview group (limited to Near East University Vocational School of Health Services Year 2 students) viewed; reactions of students, content analysis technique a from qualitative and quantitative research methods, has been measured within the framework of their responses through written opinion fo...

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The production of a rational film entails a proper utilization of both visual and audio elements to realize effective message delivery via ideal communication. The industry of film is an important tool to humans for mirroring, creating change and for the projection of his perfect world. The Nigerian movie industry has enjoyed so much attention both within and outside the shores of the country i.e Africa, Europe Asia and beyond. The growing popularity that has trailed the industry has been bedeviled with heavy criticism. There has been several calls from different works of life on the contents of the works been produced in great quantity with particular reference to thematic relevance of music in films which is seen by many as significantly ignored by movie producers or not properly placed in sequential arrangement of events or scenes in the play. The major concern of this paper is to understand the thematic relevance of music in Nollywood movies. The study was anchored on Apparatus ...

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essay of music in film

The Power of Music in Film: Unleashing Emotions & Enhancing Narratives

  • Published: July 31, 2023
  • By: Yellowbrick

The Role of Music in Film

Music in film serves several purposes. It can set the tone for a scene, create an emotional connection with the audience, and enhance the overall impact of a film. Music can also be used to foreshadow events, create tension, and build suspense.

One of the most significant roles of music in film is to create an emotional connection with the audience. Music has the power to evoke emotions like sadness, happiness, fear, and excitement. When used appropriately, it can help the audience connect with the characters and the story on a deeper level.

Another essential role of music in film is to enhance the narrative. Music can be used to highlight important moments in the story, like a character’s realization or a significant event. It can also be used to create a sense of time and place, like using classical music in a period drama.

Types of Music Used in Film

The type of music used in a film depends on the genre and the story. For example, horror films often use music with a slow tempo and low pitch to create a sense of danger and tension. On the other hand, romantic comedies often use upbeat and catchy songs to create a lighthearted and fun atmosphere.

Classical music is often used in period dramas to create a sense of time and place. The use of classical music can also give the film a sense of elegance and sophistication.

Pop songs are also frequently used in films, especially in romantic comedies and coming-of-age stories. Pop songs are often used to create a sense of nostalgia and to connect with a younger audience.

Music and Sound Design

Music is just one aspect of sound design in film. Sound design includes all the sounds in a film, including dialogue, sound effects, and music. Sound designers use sound to create a sense of space, time, and mood.

Music and sound design work together to create a cohesive and immersive experience for the audience. When used correctly, sound design can enhance the impact of a film and create a more memorable experience.

Career in Music for Film

If you’re interested in pursuing a career in music for film, there are several paths you can take. You can become a composer and create original music for films. You can also become a music supervisor and select music for films.

To pursue a career in music for film, you will need to have a strong background in music theory, composition, and sound design. You can gain experience by working on student films, independent films, or by creating your own music for film projects.

Key Takeaways

Music plays a significant role in film, serving several purposes such as creating an emotional connection with the audience, enhancing the narrative, and foreshadowing events. The type of music used in a film depends on the genre and the story. Music and sound design work together to create a cohesive and immersive experience for the audience.

If you’re interested in pursuing a career in music for film, gaining experience by working on student films, independent films, or creating your own music for film projects can help. Consider taking the NYU x Billboard | Music Industry Essentials online course and certificate program to gain a deeper understanding of the music industry and its role in film.

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essay of music in film

Home — Essay Samples — Entertainment — Film Analysis — The Role Music Plays in the Film a Quiet Place


The Role Music Plays in The Film a Quiet Place

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Published: May 19, 2020

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A computational lens into how music characterizes genre in film

Benjamin Ma

1 Department of Computer Science, University of Southern California, Los Angeles, CA, United States of America

Timothy Greer

Dillon knox, shrikanth narayanan.

2 Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States of America

Associated Data

All relevant data are within the paper and its Supporting information files.

Film music varies tremendously across genre in order to bring about different responses in an audience. For instance, composers may evoke passion in a romantic scene with lush string passages or inspire fear throughout horror films with inharmonious drones. This study investigates such phenomena through a quantitative evaluation of music that is associated with different film genres. We construct supervised neural network models with various pooling mechanisms to predict a film’s genre from its soundtrack. We use these models to compare handcrafted music information retrieval (MIR) features against VGGish audio embedding features, finding similar performance with the top-performing architectures. We examine the best-performing MIR feature model through permutation feature importance (PFI), determining that mel-frequency cepstral coefficient (MFCC) and tonal features are most indicative of musical differences between genres. We investigate the interaction between musical and visual features with a cross-modal analysis, and do not find compelling evidence that music characteristic of a certain genre implies low-level visual features associated with that genre. Furthermore, we provide software code to replicate this study at . This work adds to our understanding of music’s use in multi-modal contexts and offers the potential for future inquiry into human affective experiences.


Music plays a crucial role in the experience and enjoyment of film. While the narrative of movie scenes may be driven by non-musical audio and visual information, a film’s music carries a significant impact on audience interpretation of the director’s intent and style [ 1 ]. Musical moments may complement the visual information in a film; other times, they flout the affect conveyed in film’s other modalities (e.g.—visual, linguistic). In every case, however, music influences a viewer’s experience in consuming cinema’s complex, multi-modal stimuli. Analyzing how these media interact can provide filmmakers and composers insight into how to create particular holistic cinema-watching experiences.

We hypothesize that musical properties, such as timbre, pitch, and rhythm, achieve particular stylistic effects in film, and are reflected in the display and experience of a film’s accompanying visual cues, as well as its overall genre classification. In this study, we characterize differences among movies of different genres based on their film music scores. While this paper focuses on how music is used to support specific cinematic genres, created to engender particular film-watching experiences, this work can be extended to study other multi-modal content experiences, such as viewing television, advertisements, trailers, documentaries, music videos and musical theatre.

Related work

Music use across film genre.

Several studies have explored music use in cinema. Music has been such an integral part of the film-watching experience that guides for creating music for movies have existed since the Silent Film era of the early 20th century [ 2 ]. Gorbman [ 3 ] noted that music in film acts as a signifier of emotion while providing referential and narrative cues, while Rodman [ 4 ] points out that these cues can be discreetly “felt” or overtly “heard.” That stylistic musical effects and their purpose in film is well-attested provides an opportunity to study how these musical structures are used.

Previous work has made preliminary progress in this direction. Brownrigg presented a qualitative study on how music is used in different film genres [ 5 ]. He hypothesized that film genres have distinctive musical paradigms existing in tension with one another. By this token, the conventional score associated with one genre can appear in a “transplanted” scene in another genre. As an example, a Science Fiction movie may use musical conventions associated with Romance to help drive the narrative of a subplot that relates to love. In this paper, we use a multiple instance machine learning approach to study how film music may provide narrative support to scenes steeped in other film genres.

Other studies have taken a more quantitative approach, extracting audio from movies to identify affective content [ 6 , 7 ]. Gillick analyzed soundtracks from over 40,000 movies and television shows, extracting song information and audio features such as tempo, danceability, instrumentalness, and acousticness, and found that a majority of these audio features were statistically significant predictors of genre, suggesting that studying music in film can offer insights into how a movie will be perceived by its audience [ 8 ]. In this work, we use musical features and state-of-the-art neural embeddings to study film genre.

Another study that used machine learning techniques, by Austin et al., found timbral features most discriminatory in separating movie genres [ 1 ]. In prior work, soundtracks were analyzed without accounting for if or for how long the songs were used in a film. We extend these studies by investigating how timestamped musical clips that are explicitly used in a film relate to that film’s genre.

Musical-visual cross-modal analysis

Previous research has established a strong connection between the visual and musical modes as partners in delivering a comprehensive narrative experience to the viewer [ 9 – 12 ]. Cohen [ 10 ] argued that music “is one of the strongest sources of emotion in film” because it allows the viewer to subconsciously attach emotional associations to the visuals presented onscreen. Wingstedt [ 13 ] advanced this theory by proposing that music serves not only an “emotive” function, but also a “descriptive” function, which allows the soundtrack to describe the setting of the story-world (e.g., by using folk instruments for a Western setting). In combination with its emotive function, music’s descriptive function is critical in supporting (or undermining) the film genre characterized by the visuals of the film.

In this study, we use screen brightness and contrast as two low-level visual features to describe the visual mode of the film. Chen [ 14 ] found that different film genres have characteristically different average brightness and contrast values: Comedy and Romance films have higher contrast and brightness, while Horror, Sci-Fi, and Action films were visually darker with less contrast. Tarvainen [ 15 ] established statistically significant correlations between brightness and color saturation with feelings of “beauty” and “pleasantness” in film viewers, while darkness and lack of color were associated with “ugliness” and “unpleasantness.” This result is complementary to Chen’s finding: Comedy and Romance films tend to evoke “beauty” and “pleasantness,” while Action, Horror, and Sci-Fi tend to emphasize gritty, muddled, or even “unpleasant” and “ugly” emotions.

Multiple instance learning

Multiple instance learning (MIL) is a supervised machine learning method where ground truth labels are not available for every instance; instead, labels are provided for sets of instances, called “bags.” The goal of classification in this paradigm is to predict bag-level labels from information spread over instances. In our study, we treat each of the 110 films in the dataset as a bag, and each musical cue within the film as an instance. A musical cue is a single timestamped instance of a track from the soundtrack that plays in the film.

Strong assumptions about the relationship between bags and instances are common, including the standard multiple instance (MI) assumption where a bag (movie) contains a label if and only if there exists at least one instance (a cue within that movie) that is tagged with that label. In this work, we make the soft bag assumption, which allows for a negative-labeled bag to contain positive instances [ 16 ]. In other words, a film can contain musical moments characteristic of genres that are outside its own.

Simple MI is a MI method in which a summarization function is applied to all instances within a bag, resulting in a single feature vector for the entire bag. Then, any number of classification algorithms can be applied to the resulting single instance classification problem. Here, the arithmetic mean is used as a straightforward summarization function, as applied in [ 17 ].

Instance majority voting

In instance majority voting , each instance within a given bag is naïvely assigned the labels of that bag, and a classifier is trained on all instances. Bag-level labels are then assigned during inference using an aggregation scheme, such as majority voting [ 18 ]. As an example, a movie that is labeled as a “comedy” would propagate that label to the cues it contains during model training, and then majority voting across cues would be used to predict the final label for the movie during inference.

Neural network approaches

Neural network approaches within an MIL framework have been used extensively for sound event detection (SED) tasks with weak labeling. Ilse et al. [ 19 ] proposed an attention mechanism over instances and demonstrated competitive performance on several benchmark MIL datasets. Wang et al. [ 20 ] compared the performance of five MIL pooling functions, including attention, and found that linear softmax pooling produced the best results. Kong et al. [ 18 ] proposed a new feature-level attention mechanism, where attention is applied to the hidden layers of a neural network. Gururani et al. [ 21 ] used an attention pooling model for a musical instrument recognition task, and found improved performance over other architectures, including recurrent neural networks. In this work, we compare each of these approaches for the task of predicting a film’s genre from its music.

Contribution of this work

In this work, we objectively examine the effect of musical features on perception of film. We curate and release a dataset of processed features from 110 popular films and soundtracks, and share the code we use for our experiments ( ). To our knowledge, this is the first study that applies deep learning models on musical features to predict a film’s genre. Additionally, we interpret these models via a permutation feature importance analysis on MIR features. This analysis suggests which interpretable musical features are most predictive of each film genre studied. Lastly, we conduct a novel investigation on the interaction between the musical and low-level visual features of film, finding that musical and visual modes may exhibit characteristics of different genres in the same film clips. We believe that this work also sets the foundation that can be extended to help us better understand music’s role as a significant and interactive cinematic device, and how viewers respond to the cinematic experience.

Research data collection and curation

Film and soundtrack collection, soundtracks.

We collected the highest-grossing movies from 2014-2019 in-house ( We identified 110 films from this database with commercially available soundtracks that include the original motion picture score and purchased these soundtracks as MP3 digital downloads (see S1 Appendix for details).

We labeled the genres of every film in our 110-film dataset by extracting genre tags from IMDb ( Although IMDb lists 24 film genres, we only collect the tags of six genres for this study: Action, Comedy, Drama, Horror, Romance, and Science Fiction (Sci-Fi). This reduced taxonomy is well-attested in previous literature [ 1 , 22 – 24 ], and every film in our dataset represents at least one of these genres.

We use multi-label genre tags because many movies span more than one of the genres of interest. Further, we conjecture that these movie soundtracks would combine music that has characteristics from each genre in a label set. Statistics of the data set that we use is given in Table 1 .

Only 33 of the films have only one genre tag; the other 77 films are multi-genre. A list of tags for every movie is given in S1 Appendix .

Automatically extracting musical cues in film

We developed a methodology we call Score Stamper that automatically identifies and timestamps musical cues from a soundtrack that are used in its corresponding film. A given track from the soundtrack may be part of multiple cues if clips from that track appear in the film on multiple occasions.

The Score Stamper methodology uses Dejavu’s audio fingerprinting tool [ 25 ], which is robust to dialogue and sound effects. Default settings were used for all Dejavu parameters. The Score Stamper pipeline is explained in Fig 1 . At the end of the Score Stamper pipeline, each film has several “cue predictions.”

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A film is partitioned into non-overlapping five-second segments. For every segment, Dejavu will predict if a track in the film’s soundtrack is playing. Cues, or instances of a song’s use in a film, are built by combining window predictions. In this example, the “Cantina Band” cue lasts for 15 seconds because it was predicted by Dejavu in two nearby windows.

We evaluated Score Stamper’s prediction performance on a test set of three films: “The Lion King” (1994), “Love, Actually,” and “Star Wars: Episode IV—A New Hope.” These films were selected to determine if Dejavu would be robust to markedly different film genres, with different composers, directors, and instrumentation for each set of cues. Additionally, “Love Actually” uses a compilation soundtrack, while the other two films feature composed soundtracks. Musical cues were annotated manually in-house. A total of 84 cues, spanning 162 minutes, were identified across the three films.

Score Stamper’s predictions reached an average precision of 0.94 ( SD = .012) and an average recall of 0.47 ( SD = .086). We deemed these metrics acceptable for the purposes of this study, as a high precision score indicates that almost every cue prediction Dejavu provides will be correct, given that these test results generalize to the other films in our dataset. The recall is sufficient because the cues recognized are likely the most influential on audience response, as they are included on the commercial soundtrack and mixed clearly over sound effects and dialogue in the film. High recall is made difficult or impossible by several confounding factors: the omission of some songs in a film from its purchasable soundtrack, variations on the soundtrack rendition of the song, and muted placement of songs in the mix of the film’s audio.

This result also suggests that Score Stamper overcomes a limitation encountered in previous studies: in prior work, the whole soundtrack was used for analysis (which could be spurious given that soundtrack songs are sometimes not entirely used, or even used at all, in a film) [ 1 , 8 , 26 ]. By contrast, only the music found in a film is used in this analysis. Another benefit of this method is a timestamped ordering of every cue, opening up opportunity for more detailed temporal analysis of music in film.

Musical feature extraction

Mir features.

Past research in movie genre classification suggests that auditory features related to energy, pitch, and timbre are predictive of film genre [ 27 ]. We apply a similar process to [ 1 , 28 , 29 ] in this study: we extract features that relate to dynamics, pitch, rhythm, timbre, and tone using the eponymous functions in MATLAB’s MIRtoolbox [ 30 ] with default parameters. Spectral flatness and spectral crest are not available in MIRtoolbox, so we compute them using the eponymous functions in Audio Toolbox [ 31 ] with default parameters (see Table 2 ). To capture high-level information and align features extracted at different frequencies, all features are then “texture-windowed” by calculating mean and standard deviation of five-second windows with 33% overlap, as in [ 32 ].

VGGish features

In addition to the aforementioned features, we also extract embeddings from every cue using VGGish’s pretrained model [ 34 ]. In this framework, 128 features are extracted from the audio every.96 seconds, which we resample to 1 Hz to align with the MIR features. These embeddings have shown promise in tasks like audio classification [ 35 ], music recommendation [ 36 ], and movie event detection [ 37 ]. We compare the utility of these features with that of the MIR features.

Visual features

Following previous works in low-level visual analysis of films [ 14 , 15 ], we extract two features from each film in our dataset: brightness and contrast. These features were sampled at 1 Hz to align with musical features. Brightness and contrast were calculated as in [ 14 ], given by:

where C is the contrast, P t is the set of all pixels onscreen at timestep t , B t ( p ) is the brightness at pixel p at timestep t , and B Max and B Min refer to the maximum and minimum average brightness across pixels, evaluated per timestep.

Genre prediction model training procedure

In order to select the model architecture which could best predict film genre from musical features, we use leave-one-out cross-validation, meaning that a model is trained for each of the 110 films in the corpus using the other 109 films. As the ground truth label for each movie can contain multiple genres, the problem of predicting associated genres was posed as multi-label classification. For Simple MI and Instance Majority Voting approaches, the multi-label problem is decomposed into training independent models for each genre, in a method called binary relevance . The distribution of genre labels is unbalanced, with 55 films receiving the most common label (Action), and only 11 films receiving the least common label (Horror). In order to properly evaluate model performance across all genres, we calculate precision, recall, and F1-score separately for each genre, and then report the macro-average of each metric taken over all genres.

Model architectures

For the genre prediction task, we compare the performance of several MIL model architectures. First, we explore a Simple MI approach where instances are averaged with one of the following base classifiers: random forest (RF), support vector machine (SVM), or k-nearest neighbors (kNN). Using the same base classifiers, we also report the performance of an instance majority voting approach.

For neural network-based models, the six different pooling functions shown in Table 3 are explored. We adopt the architecture given in Fig 2 , which has achieved state-of-the-art performance on sound event detection (SED) tasks [ 18 ]. Here, the input feature representation is first passed through three dense embedding layers before going into the pooling mechanism. At the output layer, we convert the soft output to a binary prediction using a fixed threshold of 0.5. A form of weighted binary cross-entropy was used as the loss function, where weights for the binary positive and negative class for each genre are found by using the label distribution for the input training set. An Adam optimizer [ 38 ] with a learning rate of 5e-5 was used in training, and the batch size was set to 16. Each model was trained for 200 epochs.

In the multi-attention equation, L refers to the attended layer and w is a learned weight. The attention module outputs are concatenated before being passed to the output layer. In the feature-level attention equation, q (⋅) is an attention function on a representation of the input features, u (⋅).

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Frame-level and cue-level features

For each cue predicted by Score Stamper, a sequence of feature vectors grouped into frames is produced (either VGGish feature embeddings or hand-crafted MIR features). For instance, a 10-second cue represented using VGGish features will have a sequence length of 10 and a feature dimension of 128. One way to transform the problem to an MIL-compatible representation is to simply treat all frames for every cue as instances belonging to a movie-level bag, ignoring any ordering of the cues. This approach is called frame-level representation.

A simplifying approach is to construct cue-level features by averaging frame-level features per cue, resulting in a single feature vector for each cue. Using MIL terminology, these cue-level feature vectors then become the instances belonging to the film, which is a “bag.” We evaluate the performance of each model type when frame-level features are used and when cue-level features are used.

Genre prediction

Table 4 shows the performance of several model architectures on the 110-film dataset, using either VGGish features or MIR features as input. All of our models outperform both a random guess baseline, using class frequencies, and a zero rule baseline, where the most common (plurality) label set is predicted for all instances. We observe that a previous study, which predicted uni-labeled film genres from music tracks, reported a macro F1-score of 0.54 [ 1 ]. While important aspects of the two studies differ (track-level vs. film-level prediction, uni-label vs. multi-label genre tags), macro-F1 scores of 0.62 from our best-performing models demonstrate improved performance on an arguably more difficult task.

Performance metrics using leave-one-out cross-validation for each cue-level feature model are reported. IMV stands for Instance Majority Voting; FL Attn for Feature-Level Attention. Simple MI and IMV results represent performance with the best base classifier (kNN, SVM, and random forest were tried). All models reported mean-averaged precision significantly better than the random guess baseline ( p <.01), as given by a paired t-test.

We note that cue-level feature representations outperform instance-level feature representations across all models, so only values from cue-level feature models are reported. We further observe that Simple MI and IMV approaches perform better in terms of precision, recall, and F1-score when using VGGish features than when using MIR features. This result makes sense, as VGGish embeddings are already both semantically meaningful and compact, allowing for these relatively simple models to produce competitive results. Indeed, we find that Simple MI with an SVM as a base classifier on VGGish features produces the highest precision of all the models we tested. We report precision-recall curves for the top-performing MIR and VGGish models in S2 Appendix . In S3 Appendix , we present a scatter plot with precision and recall for each film (micro-averaged across all cues), for both VGGish and MIR average pooling models.

Finally, we observe that models trained using VGGish features generally outperform their counterparts trained using MIR features. Here, we note that the overall best-performing model in terms of macro-averaged F1-score is a single-attention model with 128 nodes per hidden layer, and trained using VGGish features. Interestingly, pooling mechanisms that are most consistent with the standard MI assumption—Max Pooling and Linear Softmax Pooling [ 20 ]—perform worse than other approaches. This result is consistent with the idea that a film’s genre is characterized by all the musical cues in totality, and not by a single musical moment.

Musical feature relevance scoring

To determine the the importance of different musical features toward predicting each film genre, we used the method of Permutation Feature Importance (PFI), as described in [ 39 ]. PFI scores the importance of each feature by evaluating how prediction performance degrades after randomly permuting the values of that feature across all validation set examples. The feature importance score s k for feature k is calculated as:

where F 1 p e r m k is the F1-score of the model across all leave-one-out cross-validation instances with feature k permuted, and F1 orig is the F1-score of the model without any permutations. A high score s k means that the model’s performance degraded heavily when feature k was permuted, indicating that the model relies on that feature to make predictions. This analysis was used to provide an understanding for which features contributed the most to genre predictions, not to provide the best-performing model.

To generate the F1-scores, we used our best-performing model trained on MIR features: an average-pooling model with 64 nodes per hidden layer (F1-score = 0.61). We did not analyze a model trained on VGGish features, because VGGish features are not interpretable: a PFI analysis using these features would not illuminate which human-understandable musical qualities contribute most to genre predictions. Since we had a large feature set of 140 features, and many of our features were closely related, we performed PFI on feature groups rather than individual features, as in [ 40 ]. We evaluated eight feature groups: MFCCs, ΔMFCCs, ΔΔMFCCs, Dynamics, Pitch, Rhythm, Timbre, and Tone. One feature group was created for each feature type in Table 2 (see section “Research data collection and curation”). MFCCs, ΔMFCCs, ΔΔMFCCs were separated from the “Timbre” feature type into their own feature groups, in order to prevent one group from containing a majority of the total features (and thus having an overwhelmingly high feature importance score). For each feature group, we randomly permuted all features individually from the others to remove any information encoded in the interactions between those features. We report results averaged over 100 runs in order to account for the effects of randomness. The results of our PFI analysis are shown in Fig 3 .

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Fig 3 shows that MFCCs were the highest scoring feature group in every genre. Across all genres (i.e., the “Overall” column in Fig 3 ), the next-highest scoring feature groups were Tone, Pitch, and ΔMFCCs. This corroborates past research finding MFCCs to be the best-performing feature group for various music classification tasks [ 41 , 42 ]. MFCC and ΔMFCC features were the only ones to significantly degrade performance when permuted for the Comedy and Drama genres, suggesting that those genres may be most characterized by timbral information encoded in MFCCs.

The model relied heavily on the Tone feature group in distinguishing Horror films. Brownrigg [ 5 ] qualitatively posits that atonal music or “between-pitch” sounds are characteristic of Horror film music, and the model’s reliance on Tone features–including key mode, key strength, spectral brightness, spectral entropy, and inharmonicity–supports this notion. The Tone feature group also scored highly for the Romance and Sci-Fi genres, whose scores often include modulations in texture and key strength or mode to evoke feelings of passion and wonder, respectively.

Finally, we note that the model’s predictions for Horror and Romance exhibited greater score variance during feature permutation than for the other genres, likely because Horror and Romance were under-represented in the 110-film corpus.

To investigate whether visual features associated with a genre correlate with music that the model has learned to be characteristic of that genre, we compare median screen brightness and contrast from film clips with labeled musical cues. For instance: if the model finds that music from a given film is highly characteristic of Comedy (regardless of the actual genre labels of the film), do we observe visual features in that film that are characteristic of Comedy?

We consider three different sources of genre labels: the true labels, the predicted labels from the best-performing model, and the predicted genre labels where only false positives are counted (that is, true positive genre predictions are removed from the set of all genre predictions.) By comparing the brightness and contrast averages using the actual and predicted labels, we can analyze whether musical patterns that the model finds characteristic of each genre correspond to visual patterns typical of the genre.

We use a single-attention pooling model trained on VGGish features (F1-score = 0.65). For each genre, we report the difference between the median brightness or contrast value in film clips labeled with that genre against the median value of all other clips. Table 5 shows the results.

Bold values show a statistically significant difference, as given by a Mann-Whitney U test with Bonferroni correction ( α = 0.01, m = 6) between the median of films including a given genre against those excluding it, within a given prediction source (Actual, Predicted, or False Positive).

From the “Actual” metrics, we observe that for both brightness and contrast, our dataset follows the trends illustrated in [ 14 ]: Comedy and Romance films have high average brightness and contrast, while Horror films have the lowest values for both features. However, we also note that clips from Sci-Fi films in our dataset also have high contrast, which differs from the findings of [ 14 ].

When comparing the brightness and contrast of clips by their “Predicted,” rather than “Actual,” genre, we note that the same general trends are present, but tend more toward the global median for both metrics. This movement toward the median suggests that the musical styles the model has learned to associate with each film genre do not necessarily correspond to their visual styles; e.g., a clip with music befitting Comedy may not keep the Comedy-style visual attributes of high brightness and contrast. This gap is partially explainable by the fact that the model has imperfectly learned the musical differences between genres. However, insofar as the model has learned an approximation of musical characteristics distinguishing film genres, we contend that the difference between the “Actual” and “Predicted” visual averages is an approximation of the difference between visual styles in a film’s labeled genre(s) against those genre(s) that its music alone would imply.

To further support this notion, we present the “False Positive” measure, which isolates the correlation between musical genre characteristics and visual features in movies outside that genre. For instance, in an Action movie with significant Romance musical characteristics (causing the model to assign a high Romance confidence score), do we observe visual features associated with Romance? For half of the genres’ brightness values, and a majority of the genres’ contrast values, we actually found the opposite: “False Positive” metrics tended in the opposite direction to the “Actual” metrics. This unexpected result warrants further study, but we suspect that even when musical style subverts genre expectations in a film, the visual style may stay consistent with the genre, causing the observed discrepancies between the two modes.

In this study, we quantitatively support the notion that characteristic music helps distinguish major film genres. We find that a supervised neural network model with attention pooling produces competitive results for multi-label genre classification. We use the best-performing MIR feature model to show that MFCC and tonal features are most suggestive of differences between genres. Finally, we investigate the interaction between musical and low-level visual features across film genres, but do not find evidence that music characteristic of a genre implies low-level visual features common in that genre. This work has applications in film, music, and multimedia studies.

Supporting information

S1 appendix, s2 appendix, s3 appendix, funding statement.

The study was done at the Center for Computational Media Intelligence at USC, which is supported by a research award from Google. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

  • PLoS One. 2021; 16(4): e0249957.

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: This work presents interesting and original findings about the use of music genre in films. Authors provide a proper literature review and the language is clear.

You could provide more details about p-values and statistically significance, but, honestly, I do not mind about this too much. The main issue, however, is that the difference among the models used should be inspected further. One method that I suggest is to show the results (i.e. tables 4 and 5) using violin plots, which allow a general qualitative overview of the distribution without falling in type I and II errors. Moreover, violin plots are easily to build. Another option is to just use a scatter plot in a Precision-Recall space and different colors for different models.

You say that you have used p-value for checking results in table 5, but what test have you used? Why have you chosen that test? Have you corrected it with some method (e.g. Bonferroni/Holm methods)? Have you used a multi-distribution test such as Kruskal-Wallis or ANOVA?

About the features, why have you chosen those features? Which previous studies have you followed? Have you simply used the Matlab standard features? If yes, why?

In figure 3, you show the feature importance plot for MIR features; I did not get why you have not computed the feature importance for the VGG features: reducing the number of features used for a classification model may lead to and increase of the overall performance. Again, why have you not used confidence intervals/violin plots/box plots or similar in this figure? It is hard to understand what is the importance of each feature otherwise.


Ease of reading

You should also declare more precisely the contribute of their work in the abstract and possibly in the introduction: the sentence that is at now is almost unuseful.

To my understanding, the paragraph "Visual-musical cross-modal analysis" has almost no reason to be. You repeat everything later, while previous works should be put in "Related works". Note that an extnsive survey about multi-modal and cross-modal music studies extists [1].

Paragraph "Multiple Instance Learning" is very unclear. You should say as soon as possible what is a bag and what is an instance in your study. Everything should then be referenced to your case (e.g. hypothesis etc.). This allows the reader to understand MPI with a concrete example.

Results reported about ScoreStamper in paragraph "Automatically extracting musical cues in film" are scientifically unuseful. You have tested it in only 3 bags. How many instances, stamps, multiple occasions there were? How much were differente the music pieces in the song track? Reasons about the low recall are unclear.

You should describe with more details the structure of the models used, even if these models were already used in previous papers.

[1] F. Simonetta, S. Ntalampiras, and F. Avanzini, “Multimodal Music Information Processing and Retrieval: Survey and Future Challenges,” in Proceedings of 2019 International Workshop on Multilayer Music Representation and Processing, Milan, Italy, 2019, pp. 10--18.

Reviewer #2: This paper presents a study on the use of films' soundtracks to help automatic classification of their broad genre (e.g. Action, Comedy). To do so, the authors 1) curated a new dataset of films and their corresponding sound tracks with automatic fingerprinting annotations; 2) explored different features for describing the music content of soundtracks, in particular they explored common MIR features related to dynamics, rhythm, pitch, timbre and tone (such as MFCCs, tempo and chroma) and deep learning derived features (VGGish); 3) investigated different variations classifiers and problem definitions (SVMs, kNNs, DNNs, Multiple Instance Learning, different pooling strategies); 4) performed an ablation study on the importance of the MIR features for the classification of the different genres and its relation with simple image clues such as brightness and contrast.

The paper is well written, has good references to previous work and a thoughtful discussion of the results, so I'd recommend it to be accepted. Even though the paper is in good shape already, there is room for improvement in the structure and more details about implementation and experiments should be added. See my comments below.

Improvement in structure ===

In my opinion the contributions should be better highlighted. It is a bit difficult to understand what exactly the contributions are with respect to previous work since the discussion in related work doesn't clearly highlight the limitations in the literature besides some isolated comments spread out in the text (i.e. the first clear statement I saw was in line 46). The innovations in methodology, and the analysis should be also listed as contributions if they're more extensive than previous works.

More detail on the experiments ===

Authors should explain how they assessed statistical significance. It is mentioned in the text but not clarified. Also it would be good to have more details on the network architecture besides a reference (capacity or number of parameters, layer's size, input size a bit more clear). Note that the dataset is not very big and this raises some questions on suitability of the architecture that could be partially answer with more information on the implementation.

I think some reference to the performance of previous works is needed to understand if the models presented here are performing reasonably (which they seem when I went compared to [1]) but an explicit comment would help understand the work better.

I'm not sure if I followed the conclusions in L298-L302 that the music style of a clip. The conclusion is that because the brightness and contrast in the clips using predicted labels are not correlating with the "expected behaviour" of each genre then the music in the clip doesn't necessarily correspond to the visual style? Then why do you see that effect in the clips when you use "actual labels"? Could it be an artifact of the model's performance?

Nit comments ===

- The figures are not in the main text, not sure if this is an artifact of the reviewing template or something to correct.

- L154: Briefly explain "texture windowed"

- L166: Would prefer a short explanation on how brightness and contrast were obtained and refer to paper for further details.

- L221: I don't understand this phrase, couldn't parse it.

- L283 - L286: Are you trying to say that investigating brightness and contrast mean scores on the model's predictions help understand associations between those visual features and what the model learned? And that could potentially be applied to unknown genres? Maybe rephrase to make it clearer.

Reviewer #3: The authors build on research in the field, utilizing well-constructed computational models to retrieve information and data to help us understand how film music operates across several genres and interacts with other film modalities. They have applied this to over 100 films, and I find that their approach in identifying the music from the soundtracks that are actually timestamped in the film itself is a sound and even essential one. Though this study does involve necessary technical information appropriate for a study like this, they are careful to take the reader step-by-step through their process, carefully explaining terms, and leading us to their conclusions in a logical, cogent manner. They have also provided strong data in support of the study. I believe that this study can open the way to further research, as they even suggest in the paper. As a practicing musician and musical scholar myself, I will be interested in seeing this lead to further published work that will help us better understand music’s role as significant and interactive cinematic device, and how viewers respond to the cinematic experience, emotively, perceptively, and cognitively.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3:  Yes:  Joseph L. Rivers Jr.

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Author response to Decision Letter 0

25 Jan 2021

(This response is also available in the Response to Reviewers file upload.)

To the academic editor and reviewers:

Thank you for your insightful comments on our manuscript submission. We have reviewed your comments one-by-one and prepared a revised document with changes based on your feedback. In this letter, we describe each reviewer comment and the changes we have made to address it.

We received editorial feedback to amend our Competing Interests statement to acknowledge this study’s partial funding from a corporation, Google. We have updated our Cover Letter to include an amended Competing Interests statement. Please let us know if other clarifications must be made to the manuscript in this regard.

We received feedback from reviewers advising us to provide more details about p-values and statistical significance of our results. To this end, we conducted Mann-Whitney U tests on our results from Table 5 and corrected them with Bonferroni correction.

To show that our results were replicable, we re-ran all of our experiments with leave-one-out cross-validation. We believe that this further strengthens the conclusions drawn from the experimental modeling results. At the behest of one of the reviewers, we also include scatter plots in the Precision-Recall space to better illustrate the flexibility and performance of our models.

We hope that the statistical tests that we have implemented in the revised submission will help further substantiate our experimental findings, and the conclusions drawn from our work.

One reviewer asked us to justify our choice of music information retrieval (MIR) features, as well as clarify how we calculated them. The MIR features we chose have shown utility in music emotion recognition and music genre classification from prior published work. Default parameters were used for computation and then texture-windowed to provide our models feature sets with equal window lengths. In the manuscript, we have clarified how we computed our MIR features, and cited prior works which inspired our choice for the features used in our study. Our revised submission now includes specific details of which previous studies we were following and how we computed the features we used for our study.

We thank the reviewers for inquiring why the feature importance analysis was not conducted for VGGish features. We have added a sentence in our manuscript that mentions that VGGish features are not interpretable like MIR features are (for the reason that VGGish features do not correlate with human-understandable musical descriptors, such as loudness), so a feature importance plot for VGGish features would similarly not be interpretable. Our motivation for performing PFI was not to improve model performance, but rather to get a sense for which features contributed most to genre predictions. We have amended our manuscript to address this comment. Finally, we have added confidence interval indicators to Figure 3, which displays the results of the PFI analysis.

The reviewers advised us to more explicitly state the contributions and potential applications of our work. To address this feedback, we have added in a new section to our manuscript called “Contribution of this Work,” which we hope will allow the reader to more precisely understand what we add to this area of study.

We appreciate the reviewers for pointing out a useful reference for cross-modal studies involving music. We have included the citation in our updated manuscript. Additionally, we have trimmed the “Visual-musical cross-modal analysis” section to avoid redundancy in the results section.

We received feedback that the “Multiple instance learning” sub-section in Related Works is unclear. In our most recent revision, we feel that the section is better motivated and clearer with regard to what a bag and an instance are in the context of our study. We believe the manuscript now better reflects why our problem could be framed as an MIL task, and we thank the reviewer for pointing out that this will allow the readers of our manuscript to better understand multiple instance learning and its relationship to this study.

A reviewer asked for more details on Score Stamper’s performance on three films that were manually annotated in-house. We have added to our manuscript the number of instances per movie, as well as our justification for choosing the three movies (they are of different genres, by different directors, and contain different style soundtracks).

At the request of the reviewers, we have added more information about the models and architectures that we used in our study. Additionally, we have expanded our study to include leave-one-out error analysis, which we hope provides more rigor to our study, especially given the relatively small size of our dataset. In our new manuscript, we more clearly show our method’s efficacy in predicting film genres and give more details about our models so that our study may be more easily reproduced. Finally, we have added a comparative analysis of our models’ performance against the baseline classifiers and prior studies.

Thank you for the comments about the need for additional clarity in the conclusions on “Musical-visual cross-modal analysis”. We have now added remarks on the various implications of our results. When we compare visual and musical styles by genre, the musical styles in question are the characteristics the model has learned to associate with every genre, which are not necessarily the actual musical characteristics that distinguish different film genres. Just as the model has learned an approximation of musical characteristics distinguishing film genres, our analysis approximates the relationship between visual and musical characteristics in different film genres.

Our goal in our musical-visual cross-modal analysis is to determine if visual features associated with a genre correlate with music that the model has learned to be characteristic of that same genre. We have updated the manuscript to clarify this motivation for readers.

We thank our reviewers for their interest in our manuscript and improving it. We hope that our work will find some use to our readers and others who are interested in understanding music’s role as a significant and interactive cinematic device.

Submitted filename: PLOS Rebuttal Letter FINAL.pdf

Decision Letter 1

17 Feb 2021


Please submit your revised manuscript by Apr 03 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at gro.solp@enosolp . When you're ready to submit your revision, log on to and select the 'Submissions Needing Revision' folder to locate your manuscript file.

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: (No Response)

3. Has the statistical analysis been performed appropriately and rigorously?

4. Have the authors made all data underlying the findings in their manuscript fully available?

5. Is the manuscript presented in an intelligible fashion and written in standard English?

6. Review Comments to the Author

Reviewer #1: 1) Statistical significance tests were only performed for the visual feature experiments and not for the VGG-ish and MIR features.

2) the caption of table 5 is unclear about what the bold style means: authors say to have computed p-values using bonferroni correction; this means that they have compared multiple tests, but I cannot understand what are these tests: did they compare all the tests for which they show the average at once? or did they compared "actual", "predicted" and "false positives" in each row? or all rows of "predicted" and then all rows of "actual" and then all rows of "false positives"?

3) Moreover, statistical tests need the same cardinality between the set tested. This would mean that the number of "films labeled with a given genre" is the same as the number of "films excluding the given genre", which seems unlikely. How they managed this problem for the statistical test?

4) I appreciate the addition of supplementary figure S2, but it shows recision-recall curves, not scatter plots. PR-curves comes out when the classification is made based on some threshold, and they are a method for evaluating the model, not the distributions of the predictions. AUC (Area under curve) is similar to F1-score and in this case PR-curves don't add any knowledge. Even if other reviewers find this plot useful, it's not clear what "no skill" line is. It would be more clear if MIR and VGG-ish points were on the same plot.

When I wrote "scatter plot in a precision-recall space", I was meaning a scatter plot, not a curve. Scatter plots are plots which shows points in their coordinates; in my example, coordinates were precision and recall. Points could be, for instance, each film. Multiple distributions can be plotted using different colors, eg. multiple models: see for instance the following image A plot such that could be useful to qualitatively evaluate the difference models without falling in type I and type II errors.

Reviewer #2: In my previous review I mentioned that the paper was in good shape already but I recommended the authors to 1) clarify their contributions and structure; 2) provide more details in experiments and discussion, i.e. explain details in architecture, statistical significance, how the visual features were calculated, among others.

The authors addressed all my comments and also improved the results and discussion section which is much more clear now, so I recommend the paper to be accepted in this new version.

7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files.

Author response to Decision Letter 1

We thank the reviewers for their feedback, which have helped us further strengthen our manuscript. Below we provide details of how we addressed their comments.

1) Statistical significance tests were only performed for the visual feature experiments and not for the VGG-ish and MIR features.

Response: We overlooked showing statistical significance tests for the experiments involving VGGish and MIR features in the previous manuscript. We have included a paired t-test on the mean average precision (mAP) of our models, where the null hypothesis is that the mAP of our proposed model is not shown to be significantly higher than the random guess baseline. Perhaps not surprisingly, our best-performing models show significantly better performance than the baseline at the 0.01 level. The table has been updated with more details, and we hope the readers will see that these models are significantly better-performing than our baselines.

2) The caption of table 5 is unclear about what the bold style means: authors say to have computed p-values using bonferroni correction; this means that they have compared multiple tests, but I cannot understand what are these tests: did they compare all the tests for which they show the average at once? or did they compared "actual", "predicted" and "false positives" in each row? or all rows of "predicted" and then all rows of "actual" and then all rows of "false positives"?

Response: We appreciate the feedback about our caption in table 5: we have clarified this caption to indicate that we were using 6 hypotheses in our Bonferroni Correction, which correspond to comparing the median of a particular genre in a particular column (“Actual,” “Predicted,” and “False Positives”) with the median of all of the other genres in that same column. Our caption has been reworded and proper parameters are included to elucidate the tests that we ran.

Response: While there are some statistical tests that need the same cardinality between the two samples tested, we used a Mann Whitney U test in our results reported in Table 5, which does not mandate that the number of films labeled with a given genre is the same as the number of films excluding the given genre.

4) I appreciate the addition of supplementary figure S2, but it shows precision-recall curves, not scatter plots. PR-curves comes out when the classification is made based on some threshold, and they are a method for evaluating the model, not the distributions of the predictions. AUC (Area under curve) is similar to F1-score and in this case PR-curves don't add any knowledge. Even if other reviewers find this plot useful, it's not clear what "no skill" line is. It would be more clear if MIR and VGG-ish points were on the same plot. When I wrote "scatter plot in a precision-recall space", I was meaning a scatter plot, not a curve. Scatter plots are plots which shows points in their coordinates; in my example, coordinates were precision and recall. Points could be, for instance, each film. Multiple distributions can be plotted using different colors, eg. multiple models: see for instance the following image A plot such that could be useful to qualitatively evaluate the difference models without falling in type I and type II errors.

Response: We appreciate our reviewer’s clarification about the PR plot. We have amended our manuscript to include models based on VGGish features and MIR features on the same PR plot to facilitate easy comparison of models, as was shown in the reviewer’s example. Each film’s cues were compiled and the predictions of these cues were used to calculate micro-averaged precision and recall scores for a film. We label the highest- and the lowest-precision and recall films, which we believe indicates that the performance between models using MIR features and VGGish features is similar for each film. We hope this new figure complements the PR curves shown in supplementary figure S2 by allowing readers to easily compare between MIR and VGGish-based models.

Submitted filename: Rebuttal submission3 PLOSONE.pdf

Decision Letter 2

29 Mar 2021


We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Additional Editor Comments (optional):

Reviewer #1: All comments have been addressed

Reviewer #1: All the comments have finally been addressed. Statistical significance tests have been carried out over all the evaluations shown in the text and an additional plot has been added, giving the rough idea about how different the two models are. Since PLOS One guidelines instruct the reviewers to stress the rigorousity of the scientific procedure in respect to the results, even if the plot added shows how VGG-ish features only marginally outcomes classic MIR features, I find that the paper still adds knowledge worth of publication.

Reviewer #2: The authors addressed my comments previously, and they have carefully answered and made the changes requested for the other reviewer in this iteration. The paper is in good shape to me.

Acceptance letter

31 Mar 2021

Dear Dr. Ma:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Telling Stories with Soundtracks: An Empirical Analysis of Music in Film

Jon Gillick , David Bamman

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[Telling Stories with Soundtracks: An Empirical Analysis of Music in Film]( (Gillick & Bamman, Story-NLP 2018)

  • Telling Stories with Soundtracks: An Empirical Analysis of Music in Film (Gillick & Bamman, Story-NLP 2018)
  • Jon Gillick and David Bamman. 2018. Telling Stories with Soundtracks: An Empirical Analysis of Music in Film . In Proceedings of the First Workshop on Storytelling , pages 33–42, New Orleans, Louisiana. Association for Computational Linguistics.

The Functions of Film Music: Essay Example


Music plays an important role in films. Music helps the audience to be able to capture the atmosphere of various scenes, and this enables the audience to understand the plot better.

By incorporating music in film, it is possible to arouse emotions through the characters in the movie or film so that the audience is able to share what the characters experience.

When music is played together with film, the audience is able to perceive through both sight and hearing. This paper will discuss how music has been used in the movie Curious Case of Benjamin Button.

Movie Overview

The movie is based on a short story by Scott Fitzgerald, which goes by the same title. The movie tells the story of reverse aging, and it is directed by David Fincher. The main character, Benjamin, is born while aged and grows younger and eventually dies as an infant. Music and dance have been incorporated to achieve different effects.

The music is directed by Alexandre Desplat, and features include various songs like a moment of greatness by immediate music, my body in a cage by Arcade Fire, the return by APM. Didn’t Leave Nobody but the baby, Ill fly away and a song by the beetles (twist and should). In total, the songs used in movie number forty-five and were used to produce the soundtrack and score for the movie.

The music played at the beginning of the movie is slow and soft but increases to become a little bit and then continues slowly again. The first scene in which the music plays lasts for around three minutes and five seconds during the introduction part of the movie.

When the soundtrack is played, it easily arouses emotions that lead to deep reflection, and as a consequence, the audience remains alert as it draws one towards being attentive. The soundtrack can also lead one to perceive that the visual images appearing on the screen are those of the countryside.

Therefore it can be said that the main role of the introductory music at the beginning of the movie is to set the geography and period for the movie, this mainly seeks to capture the attention of the audience before the story of the movie unfolds (Adapted from Functions of film Music by Oppenheim, 1997-2010, Para 1-4).

The music that plays in the scene where Benjamin is alone at night lasts for two and a half minutes.Through this piece of music, the audience is able to capture the feelings of sadness that characterize Benjamin’s life at this particular moment in his life, and it is conclusively be said that the use of this piece of music in this particular scene is to provide emotional focus.

A piano is used to create the musical beats used for the performance of the bethena song within the movie. The majority of the songs used for the soundtrack and score of the movie are jazz compositions by the Hall jazz band and various orchestra bands. Seven of the songs that are featured in this movie belong to the Hall Jazz Band.

The other music contributors are various orchestra groups like the Orchestra Del Teatro San Carlo, Frank Trumbauer and His Orchestra, Louis Armstrong and his cotton orchestra band and a host of other bands, individual and group singers(Reel soundtrack blog, 2008, para 1)

Based on this, it can be concluded that the source music for the movie is jazz. There is a prolonged play of music in the scene sunrise on Lake Pontchartrain, which lasts for about three and a half minutes.

This soundtrack is mainly used to capture the cool serenity associated with the lakeside, and this greatly enhances the imagination of the audience on how spectacular watching the sunrise near the lake would be like. The importance of using this piece of music at this point clearly helps to define the location in the movie (Oppenheim para 5-8).

A composition of jazz by Schubert is performed in a scene where the character Daisy goes to audition for ballet dance. Although the music plays for a very short period of time, the character Daisy is able to hear the music.

In another scene, Benjamin watches Daisy dancing during the night, and it seems like Benjamin was hearing the music playing in the background. Dasiy is also able to hear the music playing in the background again in another scene which takes place in her ballet school (Reel soundtrack blog para 1)

There is the probability that music appears in the plotline in order to build the characters of Daisy and Benjamin. The score in the two instances is sorrowful jazz, which also serves to elicit an emotional response from the audience.

Classical music has been used in the movie to set the location and time for different scenes in the movie. Classical jazz music, as used in the movie, seems to be majorly associated with the middle class. Benjamin Button and his friend Daisy are fond of Jazz music, and the music is used to develop their character and shape their roles in the movie.

Works Cited

Oppenheim, Yair. “The Functions of Film Music”.2010.

Reelsoundtrackblog. “ All 45 Songs from The Curious Case of Benjamin Button ”.2008.

Cite this paper

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In This Article Expand or collapse the "in this article" section Essay Film

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Essay Film by Yelizaveta Moss LAST REVIEWED: 24 March 2021 LAST MODIFIED: 24 March 2021 DOI: 10.1093/obo/9780199791286-0216

The term “essay film” has become increasingly used in film criticism to describe a self-reflective and self-referential documentary cinema that blurs the lines between fiction and nonfiction. Scholars unanimously agree that the first published use of the term was by Richter in 1940. Also uncontested is that Andre Bazin, in 1958, was the first to analyze a film, which was Marker’s Letter from Siberia (1958), according to the essay form. The French New Wave created a popularization of short essay films, and German New Cinema saw a resurgence in essay films due to a broad interest in examining German history. But beyond these origins of the term, scholars deviate on what exactly constitutes an essay film and how to categorize essay films. Generally, scholars fall into two camps: those who find a literary genealogy to the essay film and those who find a documentary genealogy to the essay film. The most commonly cited essay filmmakers are French and German: Marker, Resnais, Godard, and Farocki. These filmmakers are singled out for their breadth of essay film projects, as opposed to filmmakers who have made an essay film but who specialize in other genres. Though essay films have been and are being produced outside of the West, scholarship specifically addressing essay films focuses largely on France and Germany, although Solanas and Getino’s theory of “Third Cinema” and approval of certain French essay films has produced some essay film scholarship on Latin America. But the gap in scholarship on global essay film remains, with hope of being bridged by some forthcoming work. Since the term “essay film” is used so sparingly for specific films and filmmakers, the scholarship on essay film tends to take the form of single articles or chapters in either film theory or documentary anthologies and journals. Some recent scholarship has pointed out the evolutionary quality of essay films, emphasizing their ability to change form and style as a response to conventional filmmaking practices. The most recent scholarship and conference papers on essay film have shifted from an emphasis on literary essay to an emphasis on technology, arguing that essay film has the potential in the 21st century to present technology as self-conscious and self-reflexive of its role in art.

Both anthologies dedicated entirely to essay film have been published in order to fill gaps in essay film scholarship. Biemann 2003 brings the discussion of essay film into the digital age by explicitly resisting traditional German and French film and literary theory. Papazian and Eades 2016 also resists European theory by explicitly showcasing work on postcolonial and transnational essay film.

Biemann, Ursula, ed. Stuff It: The Video Essay in the Digital Age . New York: Springer, 2003.

This anthology positions Marker’s Sans Soleil (1983) as the originator of the post-structuralist essay film. In opposition to German and French film and literary theory, Biemann discusses video essays with respect to non-linear and non-logical movement of thought and a range of new media in Internet, digital imaging, and art installation. In its resistance to the French/German theory influence on essay film, this anthology makes a concerted effort to include other theoretical influences, such as transnationalism, postcolonialism, and globalization.

Papazian, Elizabeth, and Caroline Eades, eds. The Essay Film: Dialogue, Politics, Utopia . London: Wallflower, 2016.

This forthcoming anthology bridges several gaps in 21st-century essay film scholarship: non-Western cinemas, popular cinema, and digital media.

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These Sound Of Music Copycats Were Some Of The Biggest Flops In Movie History

Hello, Dolly!

Throughout the 1950s, big-budget musicals were de rigueur for Hollywood, and there was a sudden glut of epics that sported gigantic budgets, recognizable stars, and no small amount of studio hype. Such films were exhibited as touring roadshow productions, which was a great way for films to make fistfuls of cash. Roadshow epics were also, it should be noted, a concerted ploy by studios to distract audiences from the rising threat of television. Studios felt the need to invest a lot of money into musicals and epics, hoping the massive productions could draw people into theaters and keep the industry afloat.

One might logically predict, however, that Hollywood tried to ride the trend of epics for a little longer than was healthy, and foolish overspending eventually became common. The age of the "roadshow epic" pretty much came to a close with the release of the notorious bomb "Cleopatra" in 1963 . 

But then, in the mid-1960s, musicals swept back into the consciousness with a one-two-three-punch that no one saw coming. "My Fair Lady," "Mary Poppins," and "The Sound of Music" all came out in 1964 and '65, and three films combined garnered 35 Oscar nominations. "The Sound of Music" made $285 million on an $8.2 million budget. Adjusted for inflation, it's the sixth highest-grossing movie of all time. All of a sudden, a recently dead genre appeared to have new life, and studios — in a flash — immediately started overspending again. 

Unfortunately, the magic was gone, and the late '60s saw bomb after bomb as Hollywood misguidedly tried to recapture the "Sound of Music" magic. Indeed, some of those post-"Sound of Music" musicals were among the biggest bombs of all time. 

In Scott Eyman's book "20th Century-Fox: Darryl F. Zanuck and the Creation of the Modern Film Studio,"  the arc downward is laid out in painful detail.

The misguided attempt to resurrect the roadshow

To provide a little more detail, roadshows were a common release format for studio "prestige" fare. It wouldn't be until the mid-1970s that simultaneous nationwide releases became common, and studios used to send a limited number of prints out onto the world, opening in city after city, often playing for months or even years. Roadshows, unlike their limited-release cities-only brethren, didn't come with B-features, cartoons, or shorts, and were presented instead as more "theatrical." Films would be presented with intermissions, entr'actes, and exit music, and audiences would be handed programs or even tie-in merch. The studios did everything they could to make certain films feel "big."

But as stated, that format only worked for so long. After "Cleopatra" tanked — it was made for a bloated $31 million — roadshows were over and done. Dead. Sadly for the studios, the success of "The Sound of Music" indicated that the trend was back, and they perked up. "The old model will work again? Let's keep on investing," they seemed to say.

(On an unrelated note, keep an eye on what happened in Hollywood after "Spider-Man: No Way Home" was a big hit. The superhero genre — as an ascendant Hollywood trend — was dead, but a few latter-day hits are going to fool studios into continued overspending .)

And, wow, the spending! The late 1960s saw the release of many, many expensive — and not very successful — musicals from across all the Hollywood studios. Could a film like Joshua Logan's "Camelot," based on a Lerner & Lowe musical, be the next "Sound of Music?" Could Francis For Coppola's "Finian's Rainbow" with an aging Fred Astaire be the one? Could (yuck) Joshua Logan's "Paint Your Wagon" be the one?

No, no, and no.

So. Many. Bombs.

The bombs kept coming. In addition to the terrible "Paint Your Wagon," Paramount trotted out other notable turkeys like George Sidney's "Half a Sixpence," Vincente Minnelli's "On a Clear Day You Can See Forever" (with Barbra Streisand and Jack Nicholson), and Blake Edwards' "Darling Lili" with Julie Andrews. Disney wasn't free of the trends, and put out their notorious stinker "The Happiest Millionaire." Universal put out film adaptations of "Thoroughly Modern Millie" — also with Julie Andrews — and "Sweet Charity." Flicks like "Chitty Chitty Bang Bang," the not-very-good "Man of La Mancha," and the musical version of "Lost Horizon" all came during this wave. 

For Fox, 1967's "Doctor Dolittle" — a Rex Harrison musical about a man who can verbally communicate with animals — was another horrid stinker, costing the studio $17 million, and only earning back $9 million. And who could forget the disappointment of the woefully miscast "Hello, Dolly!" in 1969 , a $25 million fiasco that is still infamous to this day. Every studio tried to keep the magic alive, but none of these movies were massive hits. Indeed, most of them lost money. 

Fox studio head Richard Zanuck, the son of Darryl, went so far to refer to Robert Wise's 1968 "Star!" (cost: $14 million) as the company's Edsel, a reference to the famously horrible and horrendously unsuccessful 1958 car model put out by Ford. 

So what happened? 

The world changed. People evolved, tastes changed, and a new generation of filmmakers entered the scene. Why go see a frothy, expensive, dumb-ass musical, when the films of Ingmar Bergman are at the arthouse down the street? "Breathless" rewrote cinema. "2001: A Space Odyssey," "La Dolce Vita," and "The Battle of Algiers" all came out. "Paint Your Wagon" didn't stand a chance.

essay of music in film

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Movies, tv & music • independent film criticism • soundtrack guides • forming the future • est. 2014, passion, truth and lillian gish: the american film foundation collection releases digitally on vimeo.

American Film Foundation Essay - Lillian Gish: The Actor's Life for Me on Vimeo on Demand (American Masters)

This American Film Foundation essay contains minor spoilers for Lillian Gish: The Actor’s Life for Me. Terry Sanders’ 1988 American Masters documentary focuses on the career of American actress Lillian Gish. Check out the VV home page for more film essays , along with cast/character summaries , streaming guides and complete soundtrack song listings .

In May 2024, Terry Sanders and Freida Lee Mock’s American Film Foundation collection released digitally through Vimeo on Demand. One of the standout titles, Lillian Gish: The Actor’s Life for Me , narrated by Eva Marie Saint, spotlights the career of the silent screen’s “First Lady.” Directed by Sanders, the 1988 documentary features Gish as she recalls making her stage debut at age five in Rising Sun, Ohio before ultimately learning about the potential of “flickers” from the legendary director D.W. Griffith, who politely educated the young actress about the universal language of cinema.

Gish’s views on acting contrast heavily with those of method performers. She states, “You can’t teach acting… that’s ridiculous. You learn that from the human race and your imagination.” Even though modern movie stars may disagree, Gish’s commentaries reflect her creative innovation while trying to impress demanding industry figures such Swedish director Victor Sjöström, producer Louis B. Mayer and the aforementioned Griffith. The subject’s eyes light up as she remembers starring in “the perfect moving picture,” The Wind (1928), but Gish’s demeanor changes while discussing MGM’s preference for a happy ending, which prompted the director, Sjöström, and actor Lars Hanson to return to Sweden from Hollywood. The actress also discusses her need for a camera mirror while collaborating with Griffith, due to “too much expression.” In addition, Gish provides fascinating tales in the American Masters doc about classic Hollywood, most notably when she recalls how Mayer, the co-founder of MGM, threatened to end her career after she declined to participate in a faux scandal. “I haven’t got that much vitality,” the silent cinema star responded when thinking about a potential “offscreen and onscreen” performance. And so Gish resumed her stage career in New York City rather than pursuing roles in “talkies.”

American Film Foundation Essay: Related — The Slow Train Home

American Film Foundation Essay - Lillian Gish: The Actor's Life for Me on Vimeo on Demand (American Masters)

Gish speaks little about her personal life in Sanders’ American Masters documentary; however, the cinema icon does make it clear that she “never fell in love with an actor.” The subject says, “I was a good listener” — a personal trait that allowed her to easily learn from Griffith and screenwriters. While discussing the craft of acting, Gish states that “you must speak from your diaphragm to the lips in case something goes wrong with the throat.” She also says, “If you haven’t the imagination to be that character, then go into some other business.” If only Gish was still around to lead a MasterClass series on acting (she passed away in 1993 at age 99).

American Film Foundation Essay: Related — The Spirit of Revolution: How World Cinema Defined the 1920s

American Film Foundation Essay - Lillian Gish: The Actor's Life for Me on Vimeo on Demand (American Masters)

Gish speaks confidently and passionately about acting in Sanders’ American Masters documentary, but perhaps the most intriguing moments emerge when she discusses her love for film preservation , which the late legend describes as “one of the most important things we have in this century, particularly the newsreels.” Personally, Gish’s commentaries immediately inspired me to scroll through her filmography that culminates with a memorable role in Charles Laughton’s 1955 classic The Night of the Hunter . I’ve seen that film, but what are the Gish essentials (beyond her early work with Griffith)? It’s time to find out. As the silent screen star notes in 1987’s Whales of August (her final film role), “passion and truth… that’s all we need.”

Q.V. Hough (@QVHough) is Vague Visages’ founding editor.

American Film Foundation Essay: Related — The Face of Faith: Falconetti, Dreyer and ‘The Passion of Joan of Arc’

Categories: 1980s , 2024 Film Essays , Documentary , Featured , Film , Film Criticism by Q.V. Hough , Movies

Tagged as: 1988 , 1988 Documentary , American Film Foundation , American Masters , Film Actors , Film Actresses , Film Critic , Film Criticism , Film Director , Film Essay , Film Explained , Film Journalism , Film Publication , Film Summary , Freida Lee Mock , Journalism , Lillian Gish , Movie Actors , Movie Actresses , Movie Critic , Movie Director , Movie Essay , Movie Explained , Movie Journalism , Movie Plot , Movie Publication , Movie Summary , Q.V. Hough , Rotten Tomatoes , Streaming , Terry Sanders , Vimeo on Demand

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London Indian Film Festival Celebrates 15th Year With Nationwide Outreach, Expanded, Music, XR and Gaming Strands (EXCLUSIVE)

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“It’s been an incredible journey marking 15 years this year and we are delighted to expand into new U.K. cities, welcoming Bradford and Liverpool into our 6-city reach. It’s also personally exciting to have such a talented team and innovative partners, as we push ahead to explore new frontiers of South Asian gaming and XR scenes and continue our mission to showcase and celebrate emerging and established filmmakers, diversifying our offer to U.K. audiences,” said CEO and programming director Cary Rajinder Sawhney .

The features programme incudes “Ennennum,” a Kerala-based relationship drama, dressed as a compelling sci-fi tale; Kannada-language award winner “Mithya” and Gujarati-language “Shunya,” both coming-of-age stories following child protagonists as they navigate a rapidly changing society. The program also includes the world premiere of U.K. produced rom-com “Before Nikkah,” inspired by Richard Linklater’s “Before Sunrise.”

This year’s LIFF will play the first three episodes of Canadian series “Late Bloomer,” a comedy created by and starring social media star Jus Reign, which follows the adventures of a young Sikh millennial struggling to navigate the complexities of Eastern roots with Western ideals.

On the feminist front, the festival has programmed Marathi-language film “Sthal,” which won one of the top awards at the Toronto festival and follows a young woman’s quest for education and empowerment. Pakistani-American woman director Iram Parveen-Bilal’s “Wakhri” is inspired by real-life figures like Qandeel Baloch, Pakistan’s first social media celebrity who was murdered by her own family. It depicts a teacher, mother and social media star grappling with the challenges of trying to raise a son in a patriarchal world.

The festival has multiple short film strands. These included LGBTQIA+ film showcase, Too Desi Too Queer; the internationally programmed Satyajit Ray Short Film Competition; and the New Brit-Asian Shorts section. The Satyajit Ray competition includes five films jostling for a $1,250 (£1,000) cash prize from Civic Studios. Previous winners include Saim Sadiq, director of “Joyland,” and Shubhashish Bhutiani, director of “Hotel Salvation.”

Taking the show on the road, the festival will be holding its South Asian XR Showcase, curated by Taran Singh at the partner event, Birmingham Indian Film Festival, held at Birmingham Open Media.

The second year of the South Asian Gaming Zone in Liverpool, curated by Simran Whitman and in partnership with Tulsea, sees the festival renew its partnership with FORMAT GG.

The Manchester IFF explores how South Asians have shaped British music, showcasing stories of boundary-breakers and cultural pioneers, and fostering greater representation in British music through the lens of film.

There will also be a partnership with a major business forum, India Week, where policy and business leaders discuss India’s place in the U.K. and the world. The new event is titled The Film Conclave: Discussing the Business of Entertainment.

The festival is supported by BFI, awarding National Lottery funding, and major partner The Bagri Foundation.

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Between the Temples

Between the Temples (2024)

A cantor in a crisis of faith finds his world turned upside down when his grade school music teacher re-enters his life as his new adult Bat Mitzvah student. A cantor in a crisis of faith finds his world turned upside down when his grade school music teacher re-enters his life as his new adult Bat Mitzvah student. A cantor in a crisis of faith finds his world turned upside down when his grade school music teacher re-enters his life as his new adult Bat Mitzvah student.

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Natchitoches detectives ask for help on investigation of 6-year-old double-homicide case

NATCHITOCHES, La. (KSLA) - Natchitoches investigators are pushing to solve a fatal shooting of two men who were leaving a music studio.

On May 29, the Natchitoches Parish Sheriff’s Office (NPSO) detectives are asking for the public’s help as they continue to investigate an almost six-year-old cold case regarding a double homicide of two Natchitoches men in the Payne subdivision.

The incident happened on Aug. 8, at 1:42 a.m., NPSO’s 911 Center received multiple calls reporting shots fired in the 200 block of Michelle Drive, off of Louisiana Highway 6 East, in the Grand Ecore area of Natchitoches. Upon arrival, deputies found a 2000 Nissan passenger car in the ditch, that had struck a tree.

Inside the vehicle, deputies discovered two men suffering from multiple gunshot wounds.

Both victims were pronounced dead and identified as, driver, Cordarious Cortez Smith, 24, of 300 block of Prather Street, and passenger, Rodney D. Richards, 24, of 800 block of Brewton Street.

Reportedly, both men were leaving a music studio when they were attacked by gunfire.

At that time, retired Sheriff Victor Jones, eight investigators, and six patrol deputies processed the crime scene throughout the night with special lighting equipment brought to the scene to gather evidence.

During the early stages of the investigation, detectives canvassed the neighborhood in hopes of gathering enough probable cause and evidence, however, detectives only received limited information.

“We don’t close open criminal cases, we continue to investigate them and hopefully gather enough evidence to identify, locate, and arrest individuals for senseless crimes committed in Natchitoches Parish,” says Natchitoches Parish Sheriff Wright.

Detectives are asking the public for their help in solving this case. If you have information, contact the NPSO Criminal Investigations Bureau at 318-357-7830.  You may also be eligible for a reward by providing information to Crime Stoppers of Natchitoches at 318-238-2388.  Crime Stoppers is not a law enforcement agency.  Your tip will remain confidential.

Copyright 2024 KSLA. All rights reserved.

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Tom hardy unleashes inner monster in trailer for his final ‘venom’ movie.

The trilogy capper 'The Last Dance' arrives Oct. 25.

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Venom- Let There Be Carnage

Tom Hardy is ready to tango one last time with Venom , the Marvel anti-hero he has played for the past six years. The actor stars in the first trailer for Venom: The Last Dance , which is billed as the final film of Hardy’s trilogy in which he plays journalist Eddie Brock — a man who bonds with an alien symbiote, together becoming Venom.

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The franchise has been an outlier success for Sony as it tried to build out a universe of movies based on ancillary Spider-Man characters to which the studio has the film rights. Films like Morbius (2022) and Madame Web (2023) brought in significantly less than Hardy’s films, and The Last Dance will test the continued appetite for the franchise after several years of big-budget superhero movies struggling at the box office. The studio also has Kraven the Hunter due out Dec. 13, just weeks after The Last Dance arrives. Stars of The Last Dance include Juno Temple and Chiwetel Ejiofor.

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    Film music, whether it is a pop song, an improvised accompaniment, or an originally composed cue, can do a variety of things. It can establish setting, specifying a particular time and place; it can fashion a mood and create atmosphere; it can call attention to elements onscreen or offscreen, thus clarifying matters of plot and narrative progression; it can reinforce or foreshadow narrative ...

  6. The Role of Music in Film: How Soundtracks Enhance Storytelling

    The art of using music to enhance emotion in film is a delicate balance of subtlety and impact, requiring careful consideration of timing, tone, and theme. A poignant piece of music can underscore an emotional scene, making the audience feel the characters' joy, despair, fear, or triumph more intensely. For instance, a slow, melancholic ...

  7. (PDF) The Evolution of Music in Film and its Psychological Impact on

    This essay will focus on the psychological effect of film and television music. This essay will be using examples from the Netflicks animated adult comedy show BoJack Horseman to explore various different ways in which music may be used to affect the audience. BoJack Horseman was selected as a case study because of it's many sudden shifts in ...

  8. Analysis of the Role of Music in Film Narrative

    The Evolution of Music in Film and its Psychological Impact on Audiences. B. Herrmann. Art, Psychology. 2010. "I feel that music on the screen can seek out and intensify the inner thoughts of the characters. It can invest a scene with terror, grandeur, gaiety, or misery. It can propel narrative swiftly…. Expand. 4.

  9. [PDF] The Roles of Music in Films

    Nowadays, music and film have become intertwined, creating art that resonates with audiences on an emotional and societal level. This raises the question: How does music serve films and their audiences? This question has captured the interest of scholars, directors, and workers in the film industry, profoundly influencing the value and narratives conveyed in films.

  10. The Power of Music in Film: Unleashing Emotions & Enhancing Narratives

    Music plays a significant role in film, serving several purposes such as creating an emotional connection with the audience, enhancing the narrative, and foreshadowing events. The type of music used in a film depends on the genre and the story. Music and sound design work together to create a cohesive and immersive experience for the audience.

  11. Music in Movies

    Ultimately, the primary purpose of music in movies is to connect people to a film and make them feel certain emotions. Music's role as a storytelling aid, hinting at the character's emotions ...

  12. PDF Music in Films

    MUSIC IN FILMS BY ERNEST IRVING THE cynical musician, if such there be, contemplating such a title in such surroundings, might consider that music in films should be treated as Horrebow dealt with snakes in Iceland1 in his famous essay. But I have seen a snake in Iceland: cold, torpid and obtrusive perhaps, an

  13. The Role Music Plays in The Film a Quiet Place

    Music in film achieves a number of things. It establishes setting, creates atmosphere, calls attention to elements, foreshadows narrative developments, gives meaning to a character's actions or translates their thoughts, and creates emotion. In this short essay, I will discuss the role music plays in the film A Quiet Place.

  14. A computational lens into how music characterizes genre in film

    Introduction. Music plays a crucial role in the experience and enjoyment of film. While the narrative of movie scenes may be driven by non-musical audio and visual information, a film's music carries a significant impact on audience interpretation of the director's intent and style [].Musical moments may complement the visual information in a film; other times, they flout the affect ...

  15. Filmic Techniques: How to Analyse Music in Film or TV

    In this article, we explain how to analyse music in film or TV and show you how to write about it!

  16. Telling Stories with Soundtracks: An Empirical Analysis of Music in Film

    10.18653/v1/W18-1504. Bibkey: gillick-bamman-2018-telling. Cite (ACL): Jon Gillick and David Bamman. 2018. Telling Stories with Soundtracks: An Empirical Analysis of Music in Film. In Proceedings of the First Workshop on Storytelling, pages 33-42, New Orleans, Louisiana. Association for Computational Linguistics. Cite (Informal):

  17. Essay on The Magic of Music in Film

    Open Document. The importance of music in movies is highly regarded for manipulating the viewer's emotions and helping them immerse into the story. Music is one of the prime elements in cinema. Without it a movie would feel dull and unexciting. There are three elements in a movie: one is acting, the second is picture, and the third one is music.

  18. The Importance of Music in Film: Essay Example for Free

    Introduction. Music plays an important role in films. Music helps the audience to be able to capture the atmosphere of various scenes, and this enables the audience to understand the plot better. By incorporating music in film, it is possible to arouse emotions through the characters in the movie or film so that the audience is able to share ...

  19. Essay Film

    The term "essay film" has become increasingly used in film criticism to describe a self-reflective and self-referential documentary cinema that blurs the lines between fiction and nonfiction. Scholars unanimously agree that the first published use of the term was by Richter in 1940. Also uncontested is that Andre Bazin, in 1958, was the ...

  20. (PDF) On music's potential to convey meaning in film: A systematic

    For this review, 24 German and English empirical studies that tested music's potential to. convey meaning were identified to be compared in their research questions, the characteristics of ...

  21. Project MUSE

    As the essay film has gained in its power to convey arguments about media power, historical memory, and the desire for a different world, Black audiovisual essayism—whether that of the Black Audio Film Collective, or the call-and-response between Isaac Julien's Looking for Langston (1989) and Marlon Riggs's Tongues Untied (1989)—has long ...

  22. The Most Romantic Art of All: Music in the Classical Hollywood Cinema

    This essay is taken from a work in progress on theories of classical film music.? 1990 Board of Trustees of the University of Illinois Cinema Journal 29, No. 4, Summer 1990 35. ... film music, argued that such music was forced to serve a "hyper-explicit" illus-trative function, something clearly exemplified in the practice of "mickey-mous- ...

  23. Fox's The Sound of Music Inspired a Wave of Failed Big-Budget ...

    But then, in the mid-1960s, musicals swept back into the consciousness with a one-two-three-punch that no one saw coming. "My Fair Lady," "Mary Poppins," and "The Sound of Music" all came out in ...

  24. Marvel Composers on Crafting Music For Mutants, Gods and Heroes

    Composers for Marvel series 'Loki,' 'What If,' and 'X-Men '97' detail crafting music for mutants, gods and ultimately heroes.

  25. American Film Foundation Essay: Q.V. Hough on Lillian Gish

    Passion, Truth and Lillian Gish: The American Film Foundation Collection Releases Digitally on Vimeo. By Q.V. Hough on June 1, 2024. This American Film Foundation essay contains minor spoilers for Lillian Gish: The Actor's Life for Me. Terry Sanders' 1988 American Masters documentary focuses on the career of American actress Lillian Gish.

  26. 15th London Indian Film Festival: Wider Reach, Adds XR, Music ...

    The London Indian Film Festival celebrates its 15th edition with wider outreach across the U.K., thematic expansion that covers music, XR and gaming.

  27. Between the Temples (2024)

    Between the Temples: Directed by Nathan Silver. With Jason Schwartzman, Carol Kane, Dolly De Leon, Caroline Aaron. A cantor in a crisis of faith finds his world turned upside down when his grade school music teacher re-enters his life as his new adult Bat Mitzvah student.

  28. Pete Davidson Drives Eminem's Getaway Car in "Houdini" Music Video

    May 31, 2024 12:39pm. Pete Davidson and Eminem in "Houdini" music video Courtesy of Shady Records. Shady's back. Pete Davidson, Dr. Dre, 50 Cent, Shane Gillis, Snoop Dogg and more make a special ...

  29. Natchitoches detectives ask for help on investigation of 6-year-old

    NATCHITOCHES, La. (KSLA) - Natchitoches investigators are pushing to solve a fatal shooting of two men who were leaving a music studio. On May 29, the Natchitoches Parish Sheriff's Office (NPSO) detectives are asking for the public's help as they continue to investigate an almost six-year-old cold case regarding a double homicide of two Natchitoches men in the Payne subdivision.

  30. Tom Hardy Unleashes Inner Monster in Trailer for His Final 'Venom' Movie

    Tom Hardy is ready to tango one last time with Venom, the Marvel anti-hero he has played for the past six years. The actor stars in the first trailer for Venom: The Last Dance, which is billed as ...