  Speech enhancement aims to obtain speech signals with high intelligibility
and quality from noisy speech. Recent work has demonstrated the excellent
performance of time-domain deep learning methods, such as Conv-TasNet. However,
these methods can be degraded by the arbitrary scales of the waveform induced
by the scale-invariant signal-to-noise ratio (SI-SNR) loss. This paper proposes
a new framework called Time-domain Speech Enhancement Generative Adversarial
Network (TSEGAN), which is an extension of the generative adversarial network
(GAN) in time-domain with metric evaluation to mitigate the scaling problem,
and provide model training stability, thus achieving performance improvement.
In addition, we provide a new method based on objective function mapping for
the theoretical analysis of the performance of Metric GAN, and explain why it
is better than the Wasserstein GAN. Experiments conducted demonstrate the
effectiveness of our proposed method, and illustrate the advantage of Metric
GAN.
