Genre classification using chords and stochastic language models
| Autor: | David Rizo, Carlos Pérez-Sancho, José M. Iñesta |
|---|---|
| Přispěvatelé: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos, Reconocimiento de Formas e Inteligencia Artificial |
| Rok vydání: | 2009 |
| Předmět: |
Perplexity
Genre classification Computer science 02 engineering and technology computer.software_genre Statistical text classification Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Music information retrieval Digital audio Music psychology business.industry 020206 networking & telecommunications Pop music automation Human-Computer Interaction Lenguajes y Sistemas Informáticos Chord progressions Chord (music) 020201 artificial intelligence & image processing Artificial intelligence Language model Jazz business computer Software Natural language processing |
| Zdroj: | RUA. Repositorio Institucional de la Universidad de Alicante Universidad de Alicante (UA) |
| Popis: | 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. 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. |
| Databáze: | OpenAIRE |
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