Abstract
Speech signal is corrupted inevitably by noise which results in speech distortion during generation, transmission and reception process. In this paper, empirical mode decomposition (EMD) for non-stationary and nonlinear signal analysis is applied to speech de-noising. Moreover, focusing on the problems of envelopes fitting and interpolation points selection in conventional EMD, an improved EMD is proposed, which uses cubic hermite interpolation instead of cubic spline for signal envelopes fitting, and doubly-iterative sifting method instead of local extrema for interpolation points selection. Thus, the errors of algorithm could be reduced, and overshoots or undershoots be avoided. Simulation shows that the proposed method decreases speech distortion and increases output signal to noise ratio (SNR), compared with speech denoising based on wavelet and conventional EMD.
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