Song Emotion Recognition: a Performance Comparison Between Audio Features and Artificial Neural Networks

Authors

Keywords:

Deep learning, Neural networks, Emotion recognition, Digital signal processing, Music Information Retrieval

Abstract

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 to 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, which 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 a cappella songs.

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Referências

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Published

2022-12-30

Como Citar

Benevenuto Valadares, P. ., Rosero Jácome, K. G., dos Santos, A. N., & Sanches Masiero, B. (2022). Song Emotion Recognition: a Performance Comparison Between Audio Features and Artificial Neural Networks. Revista Eletrônica De Iniciação Científica Em Computação, 20(4). Recuperado de https://journals-sol.sbc.org.br/index.php/reic/article/view/2766