Automatic singer recognition of popular music recordings via estimation and modeling of solo vocal signals

In this paper, we investigate the problem of automatic singer identification, detection and tracking in popular music recordings with one or multiple singers. This problem reflects an important issue in multimedia applications that require the transcription and indexing of music data to meet the increasing demand for content-based information retrieval. The major challenges for this study arise from the fact that a singer's voice tends to be arbitrarily altered from time to time and is inextricably intertwined with the signal of the background accompaniment. To determine who is singing, or whether or when a particular singer is present in a music recording, methods are presented for separating vocal from nonvocal regions, for isolating singers' vocal characteristics from background music, and for distinguishing singers from one another. Experimental evaluations conducted on a pop music database consisting of solo and duet tracks confirm the validity of the proposed methods.