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A Language Modeling Approach for the Classification of Audio Music

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Resumo:The purpose of this paper is to present a method for the classification of musical pieces based on a language modeling approach. The method does not require any metadata and is used with raw audio format. It consists in 1) transforming music data into a sequence of symbols 2) building a model for each category by estimating n-grams from the sequences of symbols derived from the training set. The results obtained on three audio datasets show that, providing the amount of data is sufficient for estimating the transitions probabilities of the model, the approach performs very well. The performance achieved with the ISMIR 2004 Genre classification dataset is, to our knowledge, one of the best published in the literature.
Autores principais:Marques, Gonçalo
Outros Autores:Langlois, Thibault
Assunto:Machine Learning Music Information Retrieval
Ano:2009
País:Portugal
Tipo de documento:relatório
Tipo de acesso:acesso aberto
Instituição associada:Universidade de Lisboa
Idioma:inglês
Origem:Repositório da Universidade de Lisboa

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