The Algorithm Research Of Speech Enhancement Based On Wavelet

Spccch enhancement is a process of filtering the noisy speech signal to improve its quality and performance of speech processing system, and to make speech clearer, more understood and more comfortable. Speech enhancement is an important field of speech processing. It is an effective method to solve noise pollution and improve speech quality. Speech enhancement is very worthy researching because of speech being corrupted acoustically by ambient noise. wavelet transform is a method of signal time-frequency analysis, which possesses a great many virtues. Noise reduction based on wavelet is a superior method to abstract useful signal and lay out noise as well as mutation signal, which has expansive utility value. Wavelet is more and more applied in many field because it can more exactly analyze signal's local property. Wavelet is already deemed to grave breach in tool and method in recent years. The paper that has vital applied value researches algorithm of speech enhancement based on wavelet.The paper, firstly, researches the source, developing course and analyzes speech and noise model as well as noise estimated method. The paper has a deeply research on methods of speech enhancement and theory derivation, concluding weiner filter, kallman filter, spectral subtraction, self adapting, minimum mean square error and masking effect. The paper also puts the wavelet into speech enhancement. This method continuously proceed several wavelet decomposition to reserve the coefficient in big scale and low resolving power, meanwhile it can set a threshold for other scale wavelet coefficient, setting up zero if lower than the threshold or completely reserving coefficient if higher than threshold. At last, we can use the disposed wavelet coefficient to restructure the signal in inverse wavelet transform to recover effective signal. Wavelet transform exceeds at fourier transform owing to wavelet analyze signal in diverse scale, which is more effective than other methods in reducing musical noise and high frequency noise. Multi-resolution has obvious advances based on wavelet transform. Wavelet can effectively distinguish between mutation and noise.The paper has a lot of computer simulations in speech enhancement algorithms, such as spectral subtraction, minimum mean square error and wavelet. The contrast of simulations shows that wavelet threshold algorithm is more effective method to improve signal to noise in speech enhancement, which overcomes musical noise in nature and obtains fluency speech and low noise after noise reduction in time domain, frequency domain and speech spectrogram.