Wavelet-Based De-Noising of Speech Using Adaptive Decomposition

The keys of wavelet thresholding algorithm are to choose good wavelet, determine optimal decomposition level and select appropriate threshold. Even though much work has been done in this field, most of it was focused on the optimal choice of the threshold. In this paper, we propose an adaptive wavelet-based de-noising scheme for speech enhancement applications in the presence of additive white Gaussian noise. The proposed algorithm can adaptively select the optimal decomposition level of wavelet transformation according to the characteristics of noisy speech. The experimental results demonstrate that this proposed algorithm outperforms the classical wavelet-based de-noising method and effectively improves the practicability of this kind of algorithms.