Abstract
Music emotion classification is an area of research that helps in identifying the emotions from the songs and labelling the songs with emotion classes by extracting the features from the songs and comparing the features. Audio of music includes a great deal of acoustic information. There are several types of features within music that can influence the emotions conveyed through the audio. This paper presents a classification approach using a deep neural network that relies on acoustic features, making it apt for emotional analysis. There are 109,269 song lyrics, audio metadata with more than 13 audio features in the Music4All dataset. In sum, the findings from this work are that Artificial Neural Networks (ANN) can effectively capture all acoustic indication of all emotional cues included in the music.
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Acknowledgement
We acknowledge Igor Andre Pegoraro Santana, Fabio Pinhelli, Juliano Donini, Leonardo Catharin, Rafael Biazus Mangolin, Yandre Maldonado e Gomes da Costa, Valeria Delisandra Feltrim, and Marcos Aurelio Domingues from Department of Informatics (DIN) - State University of Maringa (UEM) - Maringa, PR, Brazil who gave permission to access to the Music4All dataset for this research work.
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Revathy, V.R., Pillai, A.S. (2022). Binary Emotion Classification of Music Using Deep Neural Networks. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_45
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DOI: https://doi.org/10.1007/978-3-030-96302-6_45
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