Reduction of musical residual noise using perceptual tools with classic speech denoising techniques

Single channel enhancement techniques based on short-time spectral amplitude (STSA) estimation have the major drawback of generating an artificial and annoying residual noise with musical character, due mainly to the unwanted peaks in the denoised signal spectrum. The detection and reduction of spectral peaks which have a musical characteristic are the main objectives of this paper. The proposed perceptual technique to reduce musical residual noise operates as a post-processing. Based on human auditory properties, the perceptual post-processing is established in a number of steps. First, we detect musical peaks by comparing tonality coefficients in each critical band of both denoised signal and reference signal. Detected musical peaks are audible only if they exceed the clean speech masking threshold (MT). However, the clean MT is not available. It is estimated by modifying the Johnston model. Secondly, we reduce the musical residual noise by removing only audible musical peaks which exceed the estimated MT. The proposed method is tested and compared with classic STSA technique and perceptual techniques at various levels of white and colored noise. Results show the validity of the proposed technique.