2019/11/08 by Diego A. Cruz, Cruz, Diego A., Sergio Sánchez López +3
Arts and Humanities · Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Diverse Musicological Studies #FOS: Computer and information sciences #Music Technology and Sound Studies #Music and Audio Processing
paper · pdf · doi:10.48550/arxiv.1911.03372
openalex publication_date 2019/11/08 · openalex created_date 2019/11/22 · openalex updated_date 2026/07/28
Colombia has a diversity of genres in traditional music, which allows to express the richness of the Colombian culture according to the region. This musical diversity is the result of a mixture of African, native Indigenous, and European influences. Organizing large collections of songs is a time consuming task that requires that a human listens to fragments of audio to identify genre, singer, year, instruments and other relevant characteristics that allow to index the song dataset. This paper presents a method to automatically identify the genre of a Colombian song by means of its audio content. The method extracts audio features that are used to train a machine learning model that learns to classify the genre. The method was evaluated in a dataset of 180 musical pieces belonging to six folkloric Colombian music genres: Bambuco, Carranga, Cumbia, Joropo, Pasillo, and Vallenato. Results show that it is possible to automatically identify the music genre in spite of the complexity of Colombian rhythms reaching an average accuracy of 69%.