Music emotion recognition: toward new, robust standards in personalized and context-sensitive applications
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...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/55773 |
| Acceso en línea: | http://hdl.handle.net/10230/55773 http://dx.doi.org/10.1109/MSP.2021.3106232 |
| Access Level: | acceso abierto |
| Palabra clave: | Emotion recognition Mood Heuristic algorithms Computational modeling Music Signal processing algorithms Prediction algorithms Information retrieval |
| Sumario: | 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. |
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