Blind Clustering of Popular Music Recordings Based on Singer Voice Characteristics
Computer Music Journal
Towards efficient automated singer identification in large music databases
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A novel framework for efficient automated singer identification in large music databases
ACM Transactions on Information Systems (TOIS)
An intelligent music playlist generator based on the time parameter with artificial neural networks
Expert Systems with Applications: An International Journal
IEEE Transactions on Audio, Speech, and Language Processing
Automatic recognition of lyrics in singing
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on atypical speech
Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
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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.