A Cepstral Mean Subtraction based features for Singer Identification

Singer IDentification (SID) is a very challenging problem in Music Information Retrieval (MIR) system. Instrumental accompaniments, quality of recording apparatus and other singing voices (in chorus) make SID very difficult and challenging research problem. In this paper, we propose SID system on large database of 500 Hindi (Bollywood) songs using state-of-the-art Mel Frequency Cepstral Coefficients (MFCC) and Cepstral Mean Subtracted (CMS) features. We compare the performance of 3^rd order polynomial classifier and Gaussian Mixture Model (GMM). With 3^rd order polynomial classifier, we achieved % SID accuracy of 78 % and 89.5 % (and Equal Error Rate (EER) of 6.75 % and 6.42 %) for MFCC and CMSMFCC, respectively. Furthermore, score-level fusion of MFCC and CMSMFCC reduced EER by 0.95 % than MFCC alone. On the other hand, GMM gave % SID accuracy of 70.75 % for both MFCC and CMSMFCC. Finally, we found that CMS-based features are effective to alleviate album effect in SID problem.