Genre classification using chords and stochastic language models

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Title: Genre classification using chords and stochastic language models
Authors: Pérez-Sancho, Carlos | Rizo, David | Iñesta, José M.
Research Group/s: Reconocimiento de Formas e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Genre classification | Statistical text classification | Chord progressions
Knowledge Area: Lenguajes y Sistemas Informáticos
Date Created: May-2009
Issue Date: Jun-2009
Publisher: Taylor & Francis
Citation: PÉREZ SANCHO, Carlos; RIZO VALERO, David; IÑESTA QUEREDA, José Manuel. “Genre classification using chords and stochastic language models”. Connection Science. Vol. 21, No. 2-3 (June 2009). ISSN 0954-0091, pp. 145-159
Abstract: Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different leve! ls of the genre hierarchy for the techniques employed are presented and discussed.
Sponsor: This work is supported by the Spanish CICyT PROSEMUS project (TIN2006-14932-C02), the research programme Consolider Ingenio 2010 (MIPRCV, CSD2007-00018) and the Pascal Network of Excellence.
ISSN: 0954-0091 (Print) | 1360-0494 (Online)
DOI: 10.1080/09540090902733780
Language: eng
Type: info:eu-repo/semantics/article
Rights: This is an electronic version of an article published in Connection Science ©2009 Copyright Taylor & Francis; Connection Science is available online at
Peer Review: si
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Appears in Collections:INV - GRFIA - Artículos de Revistas
Research funded by the EU

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