This work proposes a multichannel speech separation method with narrow-band Conformer
(named NBC). The network is trained to learn to automatically exploit narrow-band speech …

This work proposes a multichannel narrow-band speech separation network. In the short-
time Fourier transform (STFT) domain, the proposed network processes each frequency …

The end-to-end approach for single-channel speech separation has been studied recently
and shown promising results. This paper extended the previous approach and proposed a …

This paper addresses the problem of multi-channel multi-speech separation based on deep
learning techniques. In the short time Fourier transform domain, we propose an end-to-end …

Single-channel, speaker-independent speech separation methods have recently seen great
progress. However, the accuracy, latency, and computational cost of such methods remain …

In a multi-channel separation task with multiple speakers, we aim to recover all individual
speech signals from the mixture. In contrast to single-channel approaches, which rely on the …

The recent exploration of deep learning for supervised speech separation has significantly
accelerated the progress on the multi-talker speech separation problem. The multi-channel …

Recent years have witnessed a great progress in single-channel speech separation by
applying self-attention based networks. Despite the excellent performance in mining …

Multi-channel deep clustering (MDC) has acquired a good performance for speech
separation. However, MDC only applies the spatial features as the additional information. So …

Deep clustering is a deep neural network-based speech separation algorithm that first trains
the mixed component of signals with high-dimensional embeddings, and then uses a …