On the use of sequential patterns mining as temporal features for music genre classification

Music can be viewed as a sequence of sound events. However, most of current approaches to genre classification either ignore temporal information or only capture local structures within the music under analysis. In this paper, we propose the use of a song tokenization method (which transforms the mu...

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Bibliographic Details
Main Authors: Jia-Min Ren, Zhi-Sheng Chen, Jang, Jyh-Shing Roger
Format: Conference Proceeding
Language:English
Subjects:
Computer science
Data mining
Feature extraction
Hidden Markov models
Humans
Information retrieval
Long-term structure
Multiple signal classification
Music genre classification
Music information retrieval
Sequential pattern mining
Support vector machine classification
Support vector machines
Text categorization
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Summary:Music can be viewed as a sequence of sound events. However, most of current approaches to genre classification either ignore temporal information or only capture local structures within the music under analysis. In this paper, we propose the use of a song tokenization method (which transforms the music into a sequence of units) in conjunction with a data mining technique for investigating the long-term structures (also known as sequential patterns) for music genre classification. Experimental results show that the introduction of sequential patterns can effectively outperform previous approach that considers local temporal features only for music genre classification.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2010.5495955