Wavelet Denoising Based on Modified Threshold Function Optimization Method

Wavelet threshold function de-noising algorithms were effective for remove noise wavelet coefficients and preserve the useful speech coefficients from noise speech signals, thus the acquisitions of threshold function and threshold estimates can determine the efficiency of this de-noising method directly. For existing wavelet threshold functions have various deficiencies in discontinuity and different decomposition level about the fixed threshold aspect, which also produce constant errors during the de-noising process. An improved wavelet threshold function with adjusted parameters is proposed, the particle swarm optimization (PSO) algorithm is imple- nted to find the optimal parameter values of this threshold function in a noise background environ- ment, and the improved wavelet threshold function method combine with Bayes shrink threshold, furthermore, the processed optimal wavelet coefficients were reconstruct the enhancement speech signal. Compared with the traditional threshold de-noising method, the simulation results show that the modified threshold function can improve the output signal to noise ration (SNR), reduce the useful signal distortion and eliminate background noise effectively.