Folk Music Classification Using Hidden Markov Models

Automatic music classification is essential for implementing efficient music information retrieval systems; meanwhile, it may shed light on the process of human’s music perception. This paper describes our work on the classification of folk music from different countries based on their monophonic melodies using hidden Markov models. Music corpora of Irish, German and Austrian folk music in various symbolic formats were used as the data set. Different representations and HMM structures were tested and compared. The classification performances achieved 75%, 77% and 66% for 2-way classifications and 63% for 3-way classification using 6-state left-right HMM with the interval representation in the experiment. This shows that the melodies of folk music do carry some statistical features to distinguish them. We expect that the result will improve if we use a more discriminable data set and the approach should be applicable to other music classification tasks and acoustic musical signals. Furthermore, the results suggest to us a new way to think about musical style similarity.