Core estimator and adaptive gains from signal to noise ratio in a hybrid speech enhancement system

TLDR: In this article, a speech enhancement system includes a core estimator that applies to the noisy speech one of a first set of gains for each frequency bin, as well as the probability of signal absence in each bin and a level of aggression of the speech enhancement.

Abstract: A speech enhancement system receives noisy speech and produces enhanced speech. The noisy speech is characterized by a spectral amplitude spanning a plurality of frequency bins. The speech enhancement system modifies the spectral amplitude of the noisy speech without affecting the phase of the noisy speech. The speech enhancement system includes a core estimator that applies to the noisy speech one of a first set of gains for each frequency bin. A noise adaptation module segments the noisy speech into noise-only and signal-containing frames, maintains a current estimate of the noise spectrum and an estimate of the probability of signal absence in each frequency bin. A signal-to-noise ratio estimator measures an a-posteriori signal-to-noise ratio and estimates an a-priori signal-to-noise ratio based on the noise estimate. Each one of the first set of gains is based on the a-priori signal-to-noise ratio, as well as the probability of signal absence in each bin and a level of aggression of the speech enhancement. A soft decision module computes a second set of gains that is based on the a-posteriori signal-to-noise ratio and the a-priori signal-to-noise ratio, and the probability of signal absence in each frequency bin.