To address the monaural speech enhancement problem, numerous research studies
have been conducted to enhance speech via operations either in time-domain on
the inner-domain learned from the speech mixture or in time--frequency domain
on the fixed full-band short time Fourier transform (STFT) spectrograms. Very
recently, a few studies on sub-band based speech enhancement have been
proposed. By enhancing speech via operations on sub-band spectrograms, those
studies demonstrated competitive performances on the benchmark dataset of
DNS2020. Despite attractive, this new research direction has not been fully
explored and there is still room for improvement. As such, in this study, we
delve into the latest research direction and propose a sub-band based speech
enhancement system with perceptually-motivated optimization and dual
transformations, called PT-FSE. Specially, our proposed PT-FSE model improves
its backbone, a full-band and sub-band fusion model, by three efforts. First,
we design a frequency transformation module that aims to strengthen the global
frequency correlation. Then a temporal transformation is introduced to capture
long range temporal contexts. Lastly, a novel loss, with leverage of properties
of human auditory perception, is proposed to facilitate the model to focus on
low frequency enhancement. To validate the effectiveness of our proposed model,
extensive experiments are conducted on the DNS2020 dataset. Experimental
results show that our PT-FSE system achieves substantial improvements over its
backbone, but also outperforms the current state-of-the-art while being 27\%
smaller than the SOTA. With average NB-PESQ of 3.57 on the benchmark dataset,
our system offers the best speech enhancement results reported till date.

本文提出了一种基于子带的语音增强系统，名为 PT-FSE，该系统通过对子带频谱图的操作来增强语音，其中包括全频带和子频带的融合模型、频率变换模块、时间变换和人类听觉感知的损失函数，实验结果表明，该系统达到了显著的改进，并且在基准数据集上优于当前最先进的技术。