Song Emotion Recognition: A Study of the State of the Art - Santos, Arthur Nicholas dos; Rosero Jácome, Karen Gissell; Masiero, Bruno Sanches (Anais do Simpósio Brasileiro de Computação Musical (SBCM); 2021: Anais do XVIII Simpósio Brasileiro de Computação Musical, 2021)
Music is art, and art is a form of expression. Often, when a song is composed or performed, there may be an intent by the singer/songwriter of expressing some feeling or emotion through it, and, by the time the music gets in touch with an audience, a spectrum of emotional reactions can be provoked. For humans, matching the intended emotion in a musical composition or performance with the subjective perceptiveness of different listeners can be quite challenging, in account that this process is highly intertwined with people’s life experiences and cognitive capacities. Fortunately, the machine learning approach for this problem is simpler. Usually, it takes a data-set, from which features are extracted to present this data to a model, that will train to predict the highest probability of an input matching a target. In this paper, we studied the most common features and models used in recent publications to tackle music emotion recognition, revealing which ones are best suited for songs (particularly acapella).