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

  • Diego A. Cruz
  • Cristian C. Cristancho
  • Jorge E. CamargoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)

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%.

Keywords

Music genre classification Audio feature extraction Colombian music recognition 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Diego A. Cruz
    • 1
  • Cristian C. Cristancho
    • 1
  • Jorge E. Camargo
    • 1
    Email author
  1. 1.Universidad Nacional de ColombiaBogotaColombia

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