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A computational lens into how music characterizes genre in film
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 thr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8031455/ https://www.ncbi.nlm.nih.gov/pubmed/33831109 http://dx.doi.org/10.1371/journal.pone.0249957 |
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author | Ma, Benjamin Greer, Timothy Knox, Dillon Narayanan, Shrikanth |
author_facet | Ma, Benjamin Greer, Timothy Knox, Dillon Narayanan, Shrikanth |
author_sort | Ma, Benjamin |
collection | PubMed |
description | 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 https://github.com/usc-sail/mica-music-in-media. 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. |
format | Online Article Text |
id | pubmed-8031455 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-80314552021-04-14 A computational lens into how music characterizes genre in film Ma, Benjamin Greer, Timothy Knox, Dillon Narayanan, Shrikanth PLoS One Research Article 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 https://github.com/usc-sail/mica-music-in-media. 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. Public Library of Science 2021-04-08 /pmc/articles/PMC8031455/ /pubmed/33831109 http://dx.doi.org/10.1371/journal.pone.0249957 Text en © 2021 Ma et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ma, Benjamin Greer, Timothy Knox, Dillon Narayanan, Shrikanth A computational lens into how music characterizes genre in film |
title | A computational lens into how music characterizes genre in film |
title_full | A computational lens into how music characterizes genre in film |
title_fullStr | A computational lens into how music characterizes genre in film |
title_full_unstemmed | A computational lens into how music characterizes genre in film |
title_short | A computational lens into how music characterizes genre in film |
title_sort | computational lens into how music characterizes genre in film |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8031455/ https://www.ncbi.nlm.nih.gov/pubmed/33831109 http://dx.doi.org/10.1371/journal.pone.0249957 |
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