PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Genre classification of music by tonal harmony
Carlos Pérez-Sancho, David Rizo, José Iñesta, Pedro J. Ponce de León, Stefan Kersten and Rafael Ramirez
Intelligent Data Analysis Volume 14, Number 5, pp. 533-545, 2010. ISSN 1088-467X

Abstract

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Multimodal Integration
Information Retrieval & Textual Information Access
ID Code:5785
Deposited By:Carlos Pérez-Sancho
Deposited On:08 March 2010