2020/03/06 by Raul de Araújo Lima, Rômulo César Costa de Sousa, Lima, Raul de Araújo +5
Arts and Humanities · Computer Science · #68T05 (Secondary) #68T50(Primary) #Computation and Language (cs.CL) #Diverse Musicological Studies #FOS: Computer and information sciences #I.2.6 #I.2.7 #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music History and Culture #Music and Audio Processing
paper · pdf · doi:10.48550/arxiv.2003.05377
openalex publication_date 2020/03/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.