We propose PSMGAN, an efficient phase sensitive masking-based single-channel speech
enhancement technique using a conditional generative adversarial network (cGAN). The …

Speech enhancement is an essential component in robust automatic speech recognition
(ASR) systems. Most speech enhancement methods are nowadays based on neural …

Speech enhancement techniques that use a generative adversarial network (GAN) can
effectively suppress noise while allowing models to be trained end-to-end. However, such …

The success of time-frequency (TF) mask-based approaches is dependent on the accuracy
of predicted mask given the noisy spectral features. The state-of-the-art methods in TF …

Single channel speech enhancement has reached a great progress recently with the
development of deep learning. However, it is still a challenging problem to achieve …

The topic of speech enhancement has been largely improved recently, especially with the
development of generative adversarial networks (GANs). However prior methods simply …

Speech Enhancement (SE) system deals with improving the perceptual quality and
preserving the speech intelligibility of the noisy mixture. The Time-Frequency (TF) masking …

Cycle-consistent generative adversarial networks (CycleGAN) have shown their promising
performance for speech enhancement (SE), while one intractable shortcoming of these …

The speech enhancement task usually consists of removing additive noise or reverberation
that partially mask spoken utterances, affecting their intelligibility. However, little attention is …

Recently, deep neural networks (DNNs) have become the mainstream strategy for speech
enhancement task because it can achieve the higher speech quality and intelligibility than …