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Developing a benchmark for emotional analysis of music

Music emotion recognition (MER) field rapidly expanded in the last decade. Many new methods and new audio features are developed to improve the performance of MER algorithms. However, it is very difficult to compare the performance of the new methods because of the data representation diversity and...

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Autores principales: Aljanaki, Anna, Yang, Yi-Hsuan, Soleymani, Mohammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345802/
https://www.ncbi.nlm.nih.gov/pubmed/28282400
http://dx.doi.org/10.1371/journal.pone.0173392
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author Aljanaki, Anna
Yang, Yi-Hsuan
Soleymani, Mohammad
author_facet Aljanaki, Anna
Yang, Yi-Hsuan
Soleymani, Mohammad
author_sort Aljanaki, Anna
collection PubMed
description Music emotion recognition (MER) field rapidly expanded in the last decade. Many new methods and new audio features are developed to improve the performance of MER algorithms. However, it is very difficult to compare the performance of the new methods because of the data representation diversity and scarcity of publicly available data. In this paper, we address these problems by creating a data set and a benchmark for MER. The data set that we release, a MediaEval Database for Emotional Analysis in Music (DEAM), is the largest available data set of dynamic annotations (valence and arousal annotations for 1,802 songs and song excerpts licensed under Creative Commons with 2Hz time resolution). Using DEAM, we organized the ‘Emotion in Music’ task at MediaEval Multimedia Evaluation Campaign from 2013 to 2015. The benchmark attracted, in total, 21 active teams to participate in the challenge. We analyze the results of the benchmark: the winning algorithms and feature-sets. We also describe the design of the benchmark, the evaluation procedures and the data cleaning and transformations that we suggest. The results from the benchmark suggest that the recurrent neural network based approaches combined with large feature-sets work best for dynamic MER.
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spelling pubmed-53458022017-03-30 Developing a benchmark for emotional analysis of music Aljanaki, Anna Yang, Yi-Hsuan Soleymani, Mohammad PLoS One Research Article Music emotion recognition (MER) field rapidly expanded in the last decade. Many new methods and new audio features are developed to improve the performance of MER algorithms. However, it is very difficult to compare the performance of the new methods because of the data representation diversity and scarcity of publicly available data. In this paper, we address these problems by creating a data set and a benchmark for MER. The data set that we release, a MediaEval Database for Emotional Analysis in Music (DEAM), is the largest available data set of dynamic annotations (valence and arousal annotations for 1,802 songs and song excerpts licensed under Creative Commons with 2Hz time resolution). Using DEAM, we organized the ‘Emotion in Music’ task at MediaEval Multimedia Evaluation Campaign from 2013 to 2015. The benchmark attracted, in total, 21 active teams to participate in the challenge. We analyze the results of the benchmark: the winning algorithms and feature-sets. We also describe the design of the benchmark, the evaluation procedures and the data cleaning and transformations that we suggest. The results from the benchmark suggest that the recurrent neural network based approaches combined with large feature-sets work best for dynamic MER. Public Library of Science 2017-03-10 /pmc/articles/PMC5345802/ /pubmed/28282400 http://dx.doi.org/10.1371/journal.pone.0173392 Text en © 2017 Aljanaki et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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
Aljanaki, Anna
Yang, Yi-Hsuan
Soleymani, Mohammad
Developing a benchmark for emotional analysis of music
title Developing a benchmark for emotional analysis of music
title_full Developing a benchmark for emotional analysis of music
title_fullStr Developing a benchmark for emotional analysis of music
title_full_unstemmed Developing a benchmark for emotional analysis of music
title_short Developing a benchmark for emotional analysis of music
title_sort developing a benchmark for emotional analysis of music
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345802/
https://www.ncbi.nlm.nih.gov/pubmed/28282400
http://dx.doi.org/10.1371/journal.pone.0173392
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