Brazilian Lyrics-Based Music Genre Classification Using a BLSTM Network
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 . 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.
Cite
@article{arxiv.2003.05377,
title = {Brazilian Lyrics-Based Music Genre Classification Using a BLSTM Network},
author = {Raul de Araújo Lima and Rômulo César Costa de Sousa and Simone Diniz Junqueira Barbosa and Hélio Cortês Vieira Lopes},
journal= {arXiv preprint arXiv:2003.05377},
year = {2020}
}
Comments
7 pages, 4 figures, 3 tables