Speech enhancement in noisy environment using voice activity detection and wavelet thresholding

Voice activity detection (VAD) is an outstanding problem for speech transmission, enhancement and recognition. The variety and the varying nature of speech and background noise make it especially challenging. In the past years, many features emphasizing the differences between speech and noise have been proposed for their robustness. However an important problem in many areas of speech processing is the determination of presence of speech periods in a given signal. This task can be identified as a statistical hypothesis problem and its purpose is the determination to which category or class a given signal belongs. Also the classification task is often not as trivial as it appears since the increasing level of background noise leading to numerous detection errors. The selection of an adequate feature vector for signal detection and a robust decision rule is a challenging problem that affects the performance of VADs. Most algorithms are effective in numerous applications but often cause detection errors mainly due to the loss of discriminating power of the decision rule at lower SNRs. In this paper, it has been tried to extract the characteristics of noise by the VAD algorithm which can be used to smooth out the signal in silence part from the noisy environment. For further noise reduction signal then filtered in the wavelet domain using thresholding.