  Recently, deep neural networks (DNNs) have been successfully used for speech
enhancement, and DNN-based speech enhancement is becoming an attractive
research area. While time-frequency masking based on the short-time Fourier
transform (STFT) has been widely used for DNN-based speech enhancement over the
last years, time domain methods such as the time-domain audio separation
network (TasNet) have also been proposed. The most suitable method depends on
the scale of the dataset and the type of task. In this paper, we explore the
best speech enhancement algorithm on two different datasets. We propose a
STFT-based method and a loss function using problem-agnostic speech encoder
(PASE) features to improve subjective quality for the smaller dataset. Our
proposed methods are effective on the Voice Bank + DEMAND dataset and compare
favorably to other state-of-the-art methods. We also implement a low-latency
version of TasNet, which we submitted to the DNS Challenge and made public by
open-sourcing it. Our model achieves excellent performance on the DNS Challenge
dataset.
