Classification of musical patterns using variable duration hidden Markov models

This paper presents a new extension to the variable duration hidden Markov model (HMM), capable of classifying musical pattens that have been extracted from raw audio data into a set of predefined classes. Each musical pattern is converted into a sequence of music intervals by means of a fundamental frequency tracking procedure. This sequence is subsequently presented as input to a set of variable-duration HMMs. Each one of these models has been trained to recognize patterns of a corresponding predefined class. Classification is determined based on the highest recognition probability. The new type of variable-duration hidden Markov modeling proposed in this paper results in enhanced performance because 1) it deals effectively with errors that commonly originate during the feature extraction stage, and 2) it accounts for variations due to the individual expressive performance of different instrument players. To demonstrate its effectiveness, the novel classification scheme has been employed in the context of Greek traditional music, to monophonic musical patterns of a popular instrument, the Greek traditional clarinet. Although the method is also appropriate for western-style music, Greek traditional music poses extra difficulties and makes music pattern recognition a harder task. The classification results demonstrate that the new approach outperforms previous work based on conventional HMMs