A hidden Markov model based approach to music segmentation and identification

Classification of musical segments is an interesting problem. It is a key technology in the development of content-based audio document indexing and retrieval. In this paper, we apply the feature extraction and modeling techniques commonly used in automatic speech recognition to solving the problem of segmentation and instrument identification of musical passages. The correlation among the different components in the feature space and the auto-correlation of each component are analyzed to demonstrate feasibility in musical signal analysis and instrument class modeling. Our experimental results are first evaluated on 3 instrument categories, i.e. vocal music, instrumental music, and their combinations. Furthermore each category is split into two individual cases to give a 6-class problem. Our results show that good performance could be obtained with simple features, such as mel-frequency cepstral coefficients and cepstral coefficients derived from linear prediction signal analysis. Even with a limited amount of training data, we could give an accuracy of 90.60% in the case of three categories. A slightly worse accuracy of 90.38% is obtained when we double the number of categories to six classes.