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This paper describes an online algorithm for enhancing monaural noisy speech.Firstly, a novel phase-corrected low-delay gammatone filterbank is derived forsignal subband decomposition and resynthesis; the subband signals are thenanalyzed frame by frame. Secondly, a novel feature named periodicity degree(PD) is proposed to be used for detecting and estimating the fundamental period(P0) in each frame and for estimating the signal-to-noise ratio (SNR) in eachframe-subband signal unit. The PD is calculated in each unit as themultiplication of the normalized autocorrelation and the comb filter ratio, andshown to be robust in various low-SNR conditions. Thirdly, the noise energylevel in each signal unit is estimated recursively based on the estimated SNRfor units with high PD and based on the noisy signal energy level for unitswith low PD. Then the a priori SNR is estimated using a decision-directedapproach with the estimated noise level. Finally, a revised Wiener gain iscalculated, smoothed, and applied to each unit; the processed units are summedacross subbands and frames to form the enhanced signal. The P0 detectionaccuracy of the algorithm was evaluated on two corpora and showed comparableperformance on one corpus and better performance on the other corpus whencompared to a recently published pitch detection algorithm. The speechenhancement effect of the algorithm was evaluated on one corpus with twoobjective criteria and showed better performance in one highly non-stationarynoise and comparable performance in two other noises when compared to astate-of-the-art statistical-model based algorithm.

Online Monaural Speech Enhancement Based on Periodicity Analysis and A Priori SNR Estimation