Song Emotion Recognition: a Performance Comparison Between Audio Features and Artificial Neural Networks
When songs are composed or performed, there is often an intent by the singer/songwriter of expressing feelings or emotions through it. For humans, matching the emotiveness in a musical composition or performance with the subjective perceptiveness of an audience can be quite challenging. Fortunately,...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Institución: | Sociedade Brasileira de Computação (SBC) |
| Repositorio: | Revista Eletrônica de Iniciação Científica |
| Idioma: | inglés |
| OAI Identifier: | oai:journals-sol.sbc.org.br:article/2766 |
| Acceso en línea: | https://journals-sol.sbc.org.br/index.php/reic/article/view/2766 |
| Access Level: | acceso abierto |
| Palabra clave: | Deep learning Neural networks Emotion recognition Digital signal processing Music Information Retrieval |
| Sumario: | When songs are composed or performed, there is often an intent by the singer/songwriter of expressing feelings or emotions through it. For humans, matching the emotiveness in a musical composition or performance with the subjective perceptiveness of an audience can be quite challenging. Fortunately, the machine learning approach for this problem is simpler. Usually, it takes a data-set, from which audio features are extracted to present this information to a data-driven model, that will, in turn, train predicting the highest probability of an input song matching a target emotion. In this paper, we studied the most common features and models used in recent publications to tackle this problem, revealing which ones are best suited for songs a cappella. |
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