There have been numerous studies on the enhancement of the noisy speech signal. In this paper, We propose a new speech enhancement method, that is, a DFF (Dissonant Frequency Filtering) scheme combined with NR (noise reduction) algorithm. The simulation results indicate that the proposed method provides a significant gain in perceptual quality compared with the conventional method. Therefore if the proposed enhancement scheme is used as a pre-filter, the output speech quality would be enhanced perceptually.

We consider a new post-filtering algorithm for residual acoustic echo cancellation in hands-free application. The new post-filtering algorithm is composed of AR analysis, pitch prediction, and noise reduction algorithm. The residual acoustic echo is whitened via AR analysis and pitch prediction during no near-end talker period and then is cancelled by noise reduction algorithm. By removing speech characteristics of the residual acoustic echo, noise reduction algorithm reduces the power of the residual acoustic echo as well as the ambient noise. For the hands-free application in the moving car, the proposed system attenuated the interferences more than 15 dB at a constant speed of 80 km/h.

There have been numerous studies on the enhancement of the noisy speech signal. In this paper, we propose a completely new speech enhancement method, that is, a filtering of a dissonant frequency based on improved fundamental frequency estimation which is developed in frequency domain. The subjective test results indicate that the proposed method provides a significant gain in audible improvement especially for speech contaminated by colored noise and a husky voice. Therefore if the filter is employed as a pre-filter for speech enhancement, the output speech quality and intelligibility should be greatly enhanced.

In this paper, we propose a robust adaptive algorithm for impulsive noise suppression. The perturbation of the input signal as well as the perturbation of the estimation error are restricted by M-estimation. The threshold used in M-estimation is obtained from the proposed adaptive variance estimation. Simulations show that the proposed algorithm is less vulnerable to the impulsive noise than the conventional algorithm.