  It is highly desirable that speech enhancement algorithms can achieve good
performance while keeping low latency for many applications, such as digital
hearing aids, acoustically transparent hearing devices, and public address
systems. To improve the performance of traditional low-latency speech
enhancement algorithms, a deep filter-bank equalizer (FBE) framework was
proposed, which integrated a deep learning-based subband noise reduction
network with a deep learning-based shortened digital filter mapping network. In
the first network, a deep learning model was trained with a controllable small
frame shift to satisfy the low-latency demand, i.e., $\le$ 4 ms, so as to
obtain (complex) subband gains, which could be regarded as an adaptive digital
filter in each frame. In the second network, to reduce the latency, this
adaptive digital filter was implicitly shortened by a deep learning-based
framework, and was then applied to noisy speech to reconstruct the enhanced
speech without the overlap-add method. Experimental results on the WSJ0-SI84
corpus indicated that the proposed deep FBE with only 4-ms latency achieved
much better performance than traditional low-latency speech enhancement
algorithms in terms of the indices such as PESQ, STOI, and the amount of noise
reduction.
