1. Pérez-Sancho, C.; Rizo, D.; Iñesta, J.M.; Ponce de León, P.J.; Kersten, S.; Ramirez, R.

Intelligent Data Analysis, vol.14 , pp. 533-545. (2010)

In this paper we present a genre classification framework for audio music based on a symbolic classification system. Audio signals are transformed into a symbolic representation of harmony using a chord transcription algorithm, based on the computation of harmonic pitch class profiles. Then, language models built from a ground truth of chord progressions for each genre are used to perform classification. We show that chord progressions are a suitable feature to represent musical genre, as they capture the harmonic rules relevant in each musical period or style. Finally, results using both pure symbolic information and chords transcribed from audio-from-MIDI are compared, in order to evaluate the effects of the transcription process in this task.