Using simplified chords sequences to classify songs genres
Rodolfo Miranda Pereira, Carlos Nascimento Silla Junior · 2017
Music Genre Recognition (MGR) is one of the most important forms of music organization and has been an important research subject. Most MGR methods consider the audio content itself instead of the songs meta-data, such as its chords. However, the audio is not always available due to copyright issues, which makes the use of meta-data for MGR an important task. The main objective of this paper is to propose a new method for MGR using simplified chords sequences. We also propose a new public dataset with 8,994 songs containing audio and chords features from six different genres in order to evaluate our method. The experimental results reached an accuracy rate of 56.13% using only the chords sequence feature with the Random Forest classifier, and 78.40% combining audio and chords features with the SVM classifier.