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arXiv · 2003.05377

Brazilian Lyrics-Based Music Genre Classification Using a BLSTM Network

Raul de Araújo Lima·Rômulo César Costa de Sousa·Simone Diniz Junqueira Barbosa·Hélio Cortês Vieira Lopes
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Abstract

Organize songs, albums, and artists in groups with shared similarity could be done with the help of genre labels. In this paper, we present a novel approach for automatic classifying musical genre in Brazilian music using only the song lyrics. This kind of classification remains a challenge in the field of Natural Language Processing. We construct a dataset of 138,368 Brazilian song lyrics distributed in 14 genres. We apply SVM, Random Forest and a Bidirectional Long Short-Term Memory (BLSTM) network combined with different word embeddings techniques to address this classification task. Our experiments show that the BLSTM method outperforms the other models with an F1-score average of $0.48$. Some genres like "gospel", "funk-carioca" and "sertanejo", which obtained 0.89, 0.70 and 0.69 of F1-score, respectively, can be defined as the most distinct and easy to classify in the Brazilian musical genres context.

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Raul de Araújo Lima, Rômulo César Costa de Sousa, Simone Diniz Junqueira Barbosa, Hélio Cortês Vieira Lopes. 2020-03-06. Brazilian Lyrics-Based Music Genre Classification Using a BLSTM Network. https://arxiv.org/abs/2003.05377

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