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

Abstract

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.