Study of suitable Audio Feature Extract and Classification Methods to be used for Indian Classical Music's Singer Identification

Singer identification is one of the most important applications of Music Information Retrieval (MIR). The process starts with identifying first, the audio descriptors then using these feature vectors, as input, for further classification/ identification of singers using typical classifiers, such as Gaussian Mixture Model (GMM) or Hidden Markov Model (HMM). In this paper, we propose, a Hybrid method of selecting correct audio descriptors for the identification of singer of Indian Classical Music. First, only strong (primary) audio descriptors are released on the system in forward pass and the classification impact is recorded. Then only selecting the top few audio descriptors, having largest impact on the singer identification process, are selected and rest are eliminated in the backward pass. Then selecting and releasing all the less significant audio descriptors from the groups, that had maximum impact on singer identification process, increases the success of correctly identifying the singer. The method reduces substantially the large number of audio descriptors to few, important audio descriptors. The selected audio descriptors are then fed as input to further classifiers.