| 摘要(英) |
In this thesis we proposed a singer classification approach to automatically identify the singer of an unknown MP3 or AAC audio data. Differing from previous researches for singer identification in MP3 compressed domain, we use Mel-Frequency Cepstral Coefficients (MFCC) as the feature instead of MDCT (modified discrete cosine transform) coefficients. Although MFCC is often used in music classification and speaker recognition, it can not be directly obtained from compressed music data such as MP3 and AAC. In this thesis, we introduce a modified method for calculating MFCC vector in MP3 and AAC compressed domain. Besides, for describing the distribution of MFCC vectors in MFCC feature space, the GMM (Gaussian mixture model) is used. And then, for finding the nearest singer, we use maximum likelihood classification (MLC) to allot each input MFCC vector to its nearest group. Finally, we implement our approach on two embedded platforms, including Socle CDK and ITRI PAC Duo. Except the two embedded platforms, two operation system configurations are adopted, including Linux and Android. The experimental result verifies the feasibility of the proposed approach.
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| 參考文獻 |
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