  This paper describes an online algorithm for enhancing monaural noisy speech.
Firstly, a novel phase-corrected low-delay gammatone filterbank is derived for
signal subband decomposition and resynthesis; the subband signals are then
analyzed 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 each
frame-subband signal unit. The PD is calculated in each unit as the
multiplication of the normalized autocorrelation and the comb filter ratio, and
shown to be robust in various low-SNR conditions. Thirdly, the noise energy
level in each signal unit is estimated recursively based on the estimated SNR
for units with high PD and based on the noisy signal energy level for units
with low PD. Then the a priori SNR is estimated using a decision-directed
approach with the estimated noise level. Finally, a revised Wiener gain is
calculated, smoothed, and applied to each unit; the processed units are summed
across subbands and frames to form the enhanced signal. The P0 detection
accuracy of the algorithm was evaluated on two corpora and showed comparable
performance on one corpus and better performance on the other corpus when
compared to a recently published pitch detection algorithm. The speech
enhancement effect of the algorithm was evaluated on one corpus with two
objective criteria and showed better performance in one highly non-stationary
noise and comparable performance in two other noises when compared to a
state-of-the-art statistical-model based algorithm.
