Multi notch adaptive digital filter design for enhancement of speech signals embedded in non-stationary noise

This paper presents a new approach to speech enhancement based on modified least mean square-multi notch adaptive digital filter (MNADF). This approach differs from traditional speech enhancement methods since no a priori knowledge of the noise source statistics is required. Specifically, the proposed method is applied to the case where speech quality and intelligibility deteriorates in the presence of background noise. Speech coders and automatic speech recognition systems are designed to act on clean speech signals. Therefore, corrupted speech signals by the noise must be enhanced before their processing. The proposed method uses a primary input containing the corrupted speech signal and a reference input containing noise only. The new computationally efficient algorithm is developed here based on tracking significant frequencies of the noise and implementing MNADF at those frequencies. To track frequencies of the noise time-frequency analysis method such as short time frequency transform is used. Different types of noises from Noisex-92 database are used to degrade real speech signals. Objective measures, the study of the speech spectrograms and global signal-to-noise ratio (SNR), segmental SNR (segSNR) as well as subjective listing test demonstrate consistently superior enhancement performance of the proposed method over tradition speech enhancement method such as spectral subtraction.