Wavelet-based perceptual speech enhancement using adaptive threshold estimation

A new speech enhancement system, which is based on a time-frequency adaptive wavelet soft thresholding, is presented in this paper. The system utilises a Bark-scaled wavelet packet decomposition integrated into a modified Weiner filtering technique using a novel threshold estimation method based on a magnitude decision-directed approach. First, a Bark-Scaled wavelet packet transform is used to decompose the speech signal into critical bands. Threshold estimation is then performed for each wavelet band according to an adaptive noise level-tracking algorithm. Finally, the speech is estimated by incorporating the computed threshold into a Wiener filtering process, using the magnitude decision-directed approach. The proposed speech enhancement technique has been tested with various stationary and non-stationary noise cases. Reported results show that the system is capable of a high-level of noise suppression while preserving the intelligibility and naturalness of the speech.