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AI Predicts Film Genre Based on Soundtrack

A film is not complete without relevant and good music in the background. Music establishes atmosphere and mood and influences the audience’s emotional reactions as well as their interpretation of the story. A research team at the USC Viterbi School of Engineering sought to objectively examine the effect of music on cinematic genres. Their study aimed to determine if AI-based technology could predict the genre of a film based on the soundtrack alone.

While past work qualitatively indicates that different film genres have their own sets of musical conventions—conventions that make that romance film sound different from that horror movie—Narayanan and team set out to find quantitative evidence that elements of a film’s soundtrack could be used to characterise the film’s genre.

The study was the first to apply deep learning models to the music used in a film to see if a computer could predict the genre of a film based on the soundtrack alone. They found that these models were able to accurately classify a film’s genre using machine learning, supporting the notion that musical features can be powerful indicators in how people perceive different films.

This work could have valuable applications for media companies and creators in understanding how music can enhance other forms of media. It could give production companies and music supervisors a better understanding of how to create and place music in television, movies, advertisements, and documentaries in order to elicit certain emotions in viewers.

In their study, the team examined a dataset of 110 popular films released between 2014 and 2019. They used genre classification listed on the online database of information related to films to label each film as action, comedy, drama, horror, romance, or science-fiction, with many of the films spanning more than one of these genres.

They then applied a deep learning network that extracted the auditory information, like timbre, harmony, melody, rhythm, and tone from the music and score of each film. This network used machine learning to analyse these musical features and proved capable of accurately classifying the genre of each film based on these features alone.

The team also interpreted these models to determine which musical features were most indicative of differences between genres. The models didn’t give specifics as to which types of notes or instruments were associated with each genre, but they were able to establish that tonal and timbral features were most important in predicting the film’s genre.

The researchers examined the auditory information from each film using a technology known as audio fingerprinting. This technology allowed them to look at where the musical cues happen in a film and for how long. Using audio fingerprinting to listen to all of the audio from the film allowed them to overcome a limitation of previous film music studies, which usually just looked at the film’s entire soundtrack album without knowing if or when songs from the album appear in the film.

In the future, the team is interested in taking advantage of this capability to study how music is used in specific moments in a film and how musical cues dictate how the narrative of the film evolves over its course.

AI has been adopted in various areas, including healthcare. As reported by OpenGov Asia, U.S. Scientists have developed a new, automated, AI-based algorithm that can learn to read patient data from Electronic Health Records (EHR). The scientists, in a side-by-side comparison, showed that their method accurately identified patients with certain diseases as well as the traditional, “gold-standard” method, which requires much more manual labour to develop and perform.

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