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Music Emotion Recognition: Toward new, robust standards in personalized and context-sensitive applications

Gómez, J.; Cano, C.; Eerola, T.; Gomez, E.; Herrera, P.; Yang, Y.; Hu, X.

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Authors

J. Gómez

C. Cano

E. Gomez

P. Herrera

Y. Yang

X. Hu



Abstract

Emotion is one of the main reasons why people engage and interact with music [1] . Songs can express our inner feelings, produce goosebumps, bring us to tears, share an emotional state with a composer or performer, or trigger specific memories. Interest in a deeper understanding of the relationship between music and emotion has motivated researchers from various areas of knowledge for decades [2], including computational researchers. Imagine an algorithm capable of predicting the emotions that a listener perceives in a musical piece, or one that dynamically generates music that adapts to the mood of a conversation in a film—a particularly fascinating and provocative idea. These algorithms typify music emotion recognition (MER), a computational task that attempts to automatically recognize either the emotional content in music or the emotions induced by music to the listener [3] . To do so, emotionally relevant features are extracted from music. The features are processed, evaluated, and then associated with certain emotions. MER is one of the most challenging high-level music description problems in music information retrieval (MIR), an interdisciplinary research field that focuses on the development of computational systems to help humans better understand music collections. MIR integrates concepts and methodologies from several disciplines, including music theory, music psychology, neuroscience, signal processing, and machine learning.

Citation

Gómez, J., Cano, C., Eerola, T., Gomez, E., Herrera, P., Yang, Y., & Hu, X. (2021). Music Emotion Recognition: Toward new, robust standards in personalized and context-sensitive applications. IEEE Signal Processing Magazine, 38(6), 106-114. https://doi.org/10.1109/msp.2021.3106232

Journal Article Type Article
Acceptance Date Aug 27, 2021
Online Publication Date Oct 27, 2021
Publication Date 2021-11
Deposit Date Oct 4, 2021
Publicly Available Date Mar 28, 2024
Journal IEEE Signal Processing Magazine
Print ISSN 1053-5888
Electronic ISSN 1558-0792
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 38
Issue 6
Pages 106-114
DOI https://doi.org/10.1109/msp.2021.3106232

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