Combining Evidences from Mel Cepstral Features and Cepstral Mean Subtracted Features for Singer Identification

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 the strong influence of instrumental sounds. The performance of Singer Identification (SID) system is also severely affected by the quality of recording devices, transmission channels and singing voice(s) of other singer(s). We have proposed a large database of 500 songs, prepared from Hindi Bollywood songs. The State-of-the-art Mel Frequency Cepstral Coefficients (MFCC) are used as feature vectors and 2nd order polynomial classifier is employed as a pattern classifier in our work. We also used Cepstral Mean Subtraction (CMS) based MFCC (CMSMFCC) feature vectors for SID and are found to give better results than the MFCC on proposed database. The SID accuracy for MFCC and CMSMFCC was found to be 75.75% and 84.5%, respectively and Equal Error Rate (EER) for MFCC and CMSMFCC was found to be 9.48% and 8.45%, respectively. While score-level-fusion of both gave improvement in SID accuracy and EER by 10.25% and 2.08% respectively than MFCC alone.