  Recent speech enhancement methods based on convolutional neural networks
(CNNs) and transformer have been demonstrated to efficaciously capture
time-frequency (T-F) information on spectrogram. However, the correlation of
each channels of speech features is failed to explore. Theoretically, each
channel map of speech features obtained by different convolution kernels
contains information with different scales demonstrating strong correlations.
To fill this gap, we propose a novel dual-branch architecture named
channel-aware dual-branch conformer (CADB-Conformer), which effectively
explores the long range time and frequency correlations among different
channels, respectively, to extract channel relation aware time-frequency
information. Ablation studies conducted on DNS-Challenge 2020 dataset
demonstrate the importance of channel feature leveraging while showing the
significance of channel relation aware T-F information for speech enhancement.
Extensive experiments also show that the proposed model achieves superior
performance than recent methods with an attractive computational costs.
