Automatic Identification of Traditional Colombian Music Genres Based on Audio Content Analysis and Machine Learning Techniques

Abstract

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%.

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“…In the same way, within the developments and studies carried out in the country, research of a non-educational nature can be highlighted, but that strengthens the relationship between music and technology. One of these studies is [21], which proposes a software based on ML for the automatic identification of Colombian music genres. The study method evaluated a data set of 180 musical pieces belonging to six Colombian folkloric genres: Bambuco, Carranga, Cumbia, Joropo, Pasillo, and Vallenato.…”