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

• Main Author: Benevenuto Valadares, Pedro
• Publication Date: 2022
• Other Authors: Rosero Jácome, Karen Gissell, dos Santos, Arthur Nicholas, Sanches Masiero, Bruno
• Format: Article
• Language: eng
• Source: Revista Eletrônica de Iniciação Científica
• Download full: https://journals-sol.sbc.org.br/index.php/reic/article/view/2766
• topic: Deep learning; Neural networks; Emotion recognition; Digital signal processing; Music Information Retrieval; Deep learning; Neural networks; Emotion recognition; Digital signal processing; Music Information Retrieval

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.