Music Genres Classification using Text Categorization Method

Automatic music genre classification is one of the most challenging problems in music information retrieval and management of digital music database. In this paper, we propose a new framework using text category methods to classify music genres. This framework is different from current methods for music genre classification. In our framework, we consider music as text-like semantic music document, which is represented by a set of music symbol lexicons with a HMM (hidden Markov models) cluster. Music symbols can be seemed as high-level features or semantic features like beats or rhythms. We use latent semantic indexing (LSI) technique that is widely adopted in text categorization for music genre classification. From the experimental results, we could achieve an average recall over 70% for ten musical genres