Stochastic text models for music categorization

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Title: Stochastic text models for music categorization
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: Language modeling | Music categorization | Music genre classification | Music information retrieval
Knowledge Area: Lenguajes y Sistemas Informáticos | Ciencia de la Computación e Inteligencia Artificial
Issue Date: 2008
Publisher: Springer Berlin / Heidelberg
Citation: PÉREZ SANCHO, Carlos; RIZO VALERO, David; IÑESTA QUEREDA, José Manuel. "Stochastic text models for music categorization". En: Proceedings of 12th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2008 - Satellite event of the 19th International Conference of Pattern Recognition, ICPR 2008. Berlin : Springer, 2008. (Lecture Notes in Computer Science; 5342/2008), pp. 55-64
Abstract: Music genre meta-data is of paramount importance for the organization 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. This work brings to symbolic music recognition some technologies, like the stochastic language models, already successfully applied to text categorization. In this work we model chord progressions and melodies as n-grams and strings and then apply perplexity and naïve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Also a combination of the different techniques as an ensemble of classifiers is proposed. Some genres and sub-genres among popular, jazz, and academic music have been considered. The results show that the ensemble is a good trade-off approach able to perform well without the risk of choosing the wrong classifier.
Sponsor: This work is supported by the Spanish PROSEMUS project (TIN2006-14932-C02), the research programme Consolider Ingenio 2010 (MIPRCV, CSD2007-00018) and the Pascal Network of Excellence.
ISBN: 978-3-540-89688-3
ISSN: 0302-9743 (Print) | 1611-3349 (Online)
DOI: 10.1007/978-3-540-89689-0_10
Language: eng
Type: info:eu-repo/semantics/article
Rights: The original publication is available at
Peer Review: si
Publisher version:
Appears in Collections:INV - GRFIA - Artículos de Revistas
Research funded by the EU

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