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- W2392768049 abstract "This paper applies the theory of wavelet multiresolution theory to decomposing the signal.Combining with its voice features,this paper analyses the difference of the contribution of the high and low frequency on the speaker recognition.Experimental results show that the rate of detail coefficients after a layer of decomposition is 94.4%,increased by 7% than the MFCC of the original signal,and the number of the data cuts by half than the original signal.The second dissolved high frequencies voice still achieves higher recognition rate,and the extracted low-frequency signal also can achieve 70.8% recognition rate." @default.
- W2392768049 created "2016-06-24" @default.
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- W2392768049 date "2013-01-01" @default.
- W2392768049 modified "2023-09-28" @default.
- W2392768049 title "Application of wavelet multiresolution theory to extract personality characteristics" @default.
- W2392768049 hasPublicationYear "2013" @default.
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