New Speech Enhancement Method based on Wavelet Transform and Tracking of Non Stationary Noise Algorithm

In this work, we have developed an efficient approach for enhancing speech by combining tracking of non stationary noise algorithm and Continues Wavelet Transform (CWT). Tracking of non stationary noise method that is based on data-driven recursive noise power estimation was proposed by Jan S. Erkelens and Richard Heusdens. The Continues Wavelet decomposition of speech signal uses adaptive level with Harr mother wavelet. In this paper, our novel method was evaluated in presence of different kind of noise using the NOIZEUS noisy speech corpus developed in Hu and Loizou laboratory that is suitable for evaluation of speech enhancement algorithms. The noisy database contains 30 IEEE sentences (produced by three male and three female speakers) corrupted by eight different real -world noises at different SNRs. The noise was taken from the AURORA database and includes suburban train noise, babble, car, exhibition hall , restaurant , street , airport and train-station noise. For evaluating the performance of speech enhancement methods we have used Perceptual Evaluation of Speech Quality scores (PESQ, ITU-T P.862). Simulation results demonstrate that the proposed approach offers an improved performance of speech enhancement in comparison with state-of-the-art methods in terms of PESQ measure. Keywords— Speech enhancement, Tracking of non stationary noise method, Wavelet Transform; PESQ.