Analysis and application of audio features extraction and classification method to be used for North Indian Classical Music’s singer identification problem

The Singer identification process requires extraction of useful musical information and classification. In literature, various methods of extracting the features of audio signal have been proposed. Depending upon the application, for which the information is to be extracted, there are various approaches of extraction and viewpoints for the signal analysis. The features are mainly analysed in time or frequency domains. Different classifiers such as Kmeans clustering, Hidden Markov model etc. have been utilized according to the applications such as singing voice detection, musical instrument classification or genre recognition. The performance efficiencies of these classifiers differ with difference in input, feature extractors used and application for which classification has been done. In this paper, we have analysed majority of the contributions done in this regards and have proposed the best suitable audio feature descriptor and the classifiers to be used for the problem of Singer identification in North Indian classical music. This type of music requires special attention and careful selection of feature extractors because of the involvement of accompanying instruments and melodic structure of the raga. There exist more than 52 audio descriptors in literature including all low level descriptors specified in MPEG7 standards. If all of them are considered as features to be used for classification and probabilistic models of classification are used then the system becomes complex and messy. In contrast to the western music, which is harmonious in nature, north Indian classical music is more complex structure and requires perceptual analysis along with less number of audio descriptors and a simple method of classification so as to reduce the computational complexity of the system. We have analysed various approaches and then proposed and implemented a singer identification process that reduces the complexity and increase the efficiency of solution to the problem of identification of a singer in North Indian Classical Music. The efficiency achieved by combining RMS energy, Brightness and Fundamental Frequency has been found to be 70% when K-means clustering has been used for classification of the singer of north Indian classical musicvocal.