Abstract：Monaural speech enhancement algorithms obtain enhanced speech by estimating and negating the noise components in speech with noise. However, the over-estimation and the error of the introduction to make up the over-estimation of noise power make detrimental effect on the enhanced speech. To constrain the distortion caused by noise over-estimation, a time-frequency mask estimation and optimization algorithm based on Computational Auditory Scene Analysis (CASA) was proposed. Firstly, Decision Directed (DD) algorithm was used to estimate the priori Signal-to-Noise Ratio (SNR) and calculate the initial mask. Secondly, the Inter-Channel Correlation (ICC) factor between noise and speech with noise in each Gammatone filterbank channel was used to calculate the noise presence probability, the new noise estimation was obtained by the probability combining with the power spectrum of speech with noise, and the over-estimation of the primary estimated noise was decreased. Thirdly, the initial mask was iterated by the optimization algorithm to reduce the error caused by the noise over-estimation and raise the target speech components in the mask, and the new mask was obtained when the iteration stopped with the conditions met. Finally, the optimization method was used to optimize the estimated mask. The enhanced speech was composed by using the new mask. Experimental results demonstrate that the new mask has higher Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility measure (STOI) values of the enhanced speech in comparison with the mask before optimization, improving the intelligibility and listening feeling of speech.
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2019, Vol. 39 
