An improved soft decision based noise power estimation employing adaptive prior and conditional smoothing

Abstract

In this paper, a new approach is proposed to improve a sigmoid and conditional smoothing-based speech presence probability (SPP) method for noise power spectral density (PSD) estimation. In this approach, the a posteriori speech absence probability (SAP) is adapted with a sigmoid function mapped to the normalised spectral average variance in the consecutive frames that can effectively characterise noise variation. The adaptation is also employed in the conditional smoothing stage to characterise the a posteriori SPP, which is then utilised in the noise PSD estimation. Comparison with state of the art methods validates the effectiveness of the proposed method.