Abstract
Singer recognition is an important branch of music retrieval and classification. This paper focuses on the application of convolutional deep belief networks (CDBN) for singer recognition. First, the system architecture of singer recognition based on CDBN is given, and then the pre-processing of the song signal is described in detail, including sampling, framing, pre-emphasis and windowing. The feature extraction of song signal based on MFCC is described in detail, and the composition and principle of CDBN and its application in singer recognition are introduced. Experiment based on three different feature extraction techniques of LPCC, MFCC and CDBN is carried out and the result is compared and analysed, the experimental results show that CDBN is effective for singer recognition.
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