A Hybrid Selection Method of Audio Descriptors for Singer Identification in North Indian Classical Music

Singer identification is most important application of Music information retrieval. The process starts with identifying first the audio descriptors then using these feature vectors as input to further classification using Gaussian Mixture Model or Hidden Markov Model as classifiers to identify the singer. The process becomes chaotic if all audio descriptors are used for finding the feature vector, instead if the audio descriptors are selected with respect to the application then the process becomes comparatively simple. In this paper we propose a Hybrid method of selecting correct audio descriptors for the identification of singer of North Indian Classical Music. First only strong (primary) audio descriptors are released on the system in forward pass and the classification impact is to be 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.