  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\%.
