Automatic Music Genre Classification Using a Hierarchical Clustering and a Language Model Approach

Automatic music genre classification has received a lot of attention from the music information retrieval (MIR) community in the past years. Systems capable of discriminating music genres are essential for managing music databases. This paper presents a method for music genre classification based solely on the audio contents of the signal. The method relies on a language modeling approach and takes in account the temporal information of the music signals for genre classification. First, the music data is transformed into a sequence of symbols, and a model is derived for each genre by estimating n-grams from the training data. As a term o comparison, HMMs models for each musical genre were also implemented. Tests on different audio sets show that the proposed approach performs very well, and outperforms HMMs based methods.