Weighted Sigmoid-Based Frequency-Selective Noise Filtering for Speech Denoising

Abstract

Estimation of noise often has a major impact on the quality of enhanced signal, especially when it comes in speech enhancement applications. The non-stationary noise statistics vary with time, making decision of speech active/inactive frame is however difficult. Further, since there is no prior information of noise distribution, the estimators use the recursive averaging with a fixed smoothing coefficient ranging from 0.70 to 0.99. This fixed smoothing coefficient actually correlates the previous frames of noise statistics. Unfortunately, using fixed smoothing coefficient, the estimator treats both speech active/inactive frames equally which may cause the leakage of speech/noise power and results in loss of speech intelligibility. To address this problem and to increase the noise estimation accuracy, this paper proposes a posteriori SNR and frequency dependent adaptive smoothing coefficient. Further, this paper investigates the performance of proposed weighted sigmoid function (WSIG) noise estimator. From both objective and subjective quality assessments, it is clearly evident that the proposed noise estimator yields considerably better tracking of noise spectral variations compared to the existing state of the art methods.