Feature based approach for singer identification

Abstract

One of the challenging and difficult problems under the category of Music Information Retrieval (MIR) is to identify a singer of a given song under strong instrumental accompaniments. Besides instrumental sounds, other parameters are also severely affecting Singer IDentification (SID) accuracy, such as quality of song recording devices, transmission channels and other singing voices present within a song. In our work, we propose singer identification with large database of 500 songs (largest database ever used in any of the SID problem) prepared from Hindi (Indian Language) Bollywood songs. In addition, vocal portions are segmented manually from each of the songs. Different features have been employed in addition to state-of-the-art feature set, Mel Frequency Cepstral Coefficients (MFCC) in this thesis work. To identify a singer, three classifiers are employed, viz., 2nd order polynomial classifier, 3rd order polynomial classifier and state-of-the-art GMM classifier. Furthermore, to alleviate the effect of recording devices and transmission channels, Cepstral Mean Subtraction (CMS) technique on MFCC is utilized for singer identification and it is providing better results than the baseline MFCC alone. Moreover, the 3rd order classifier outperforms amongst all three classifiers. Score-level fusion technique of MFCC and CMSMFCC is also used in this thesis and it improves the results significantly.