Discovering Time-Constrained Sequential Patterns for Music Genre Classification

Abstract

A music piece can be considered as a sequence of sound events which represent both short-term and long-term temporal information. However, in the task of automatic music genre classification, most of text-categorization-based approaches could only capture temporal local dependencies (e.g., unigram and bigram-based occurrence statistics) to represent music contents. In this paper, we propose the use of time-constrained sequential patterns (TSPs) as effective features for music genre classification. First of all, an automatic language identification technique is performed to tokenize each music piece into a sequence of hidden Markov model indices. Then TSP mining is applied to discover genre-specific TSPs, followed by the computation of occurrence frequencies of TSPs in each music piece. Finally, support vector machine classifiers are employed based on these occurrence frequencies to perform the classification task. Experiments conducted on two widely used datasets for music genre classification, GTZAN and ISMIR2004Genre, show that the proposed method can discover more discriminative temporal structures and achieve a better recognition accuracy than the unigram and bigram-based statistical approach.