Auditory mask estimation by RPCA for monaural speech enhancement

Mask estimation has shown a IoT of promise in speech enhancement for its simplicity and large speech intelligibility improvement. In this paper, the gammachirp filter banks are applied on the contaminated speech signal to get the auditory time-frequency representation. Robust principal component analysis with non-negative constraint is employed to decompose the auditory time-frequency representation into sparse and low-rank components using alternating direction method of multipliers optimization algorithm. Auditory Mask is estimated by these two parts which are correspond to the speech and noise. Consider that binary mask produces separated sources with more distortion than soft mask estimation. Auditory mask estimation is based on the ideal ratio mask estimation. Experimental results show that the proposed method could achieve better performance in terms of PESQ and LSD compared with multiband spectral subtraction and Robust principal component analysis methods.