Noise Estimation Using Mean Square Cross Prediction Error for Speech Enhancement

Abstract

This paper shows the feasibility of noise extraction from noisy speech and presents a two-stage approach for speech enhancement. The preproposed mean square cross prediction error (MSCPE) based blind source extraction algorithm is utilized to extract the additive noise from the noisy speech signal in the first stage. After that a modified spectral subtraction and a modified Wiener filter approach are proposed to extract the speech signal for speech enhancement in the second stage, where all the frequency spectra of the extracted noise are utilized. Theoretical justification shows that the MSCPE-based algorithm can extract desired signal from mixed sources. Experimental results show that the averaged correlation coefficient between the extracted noise and the original additive noise are beyond 85% for Gaussian noise and beyond 75% for real-world noise at SNR = 0 dB, and the proposed speech enhancement approaches perform better than conventional methods, such as spectral subtraction and Wiener filter.