844 research outputs found
An Efficient Speech Separation Network Based on Recurrent Fusion Dilated Convolution and Channel Attention
We present an efficient speech separation neural network, ARFDCN, which
combines dilated convolutions, multi-scale fusion (MSF), and channel attention
to overcome the limited receptive field of convolution-based networks and the
high computational cost of transformer-based networks. The suggested network
architecture is encoder-decoder based. By using dilated convolutions with
gradually increasing dilation value to learn local and global features and
fusing them at adjacent stages, the model can learn rich feature content.
Meanwhile, by adding channel attention modules to the network, the model can
extract channel weights, learn more important features, and thus improve its
expressive power and robustness. Experimental results indicate that the model
achieves a decent balance between performance and computational efficiency,
making it a promising alternative to current mainstream models for practical
applications.Comment: Accepted by Interspeech 202
Efficient Encoder-Decoder and Dual-Path Conformer for Comprehensive Feature Learning in Speech Enhancement
Current speech enhancement (SE) research has largely neglected channel
attention and spatial attention, and encoder-decoder architecture-based
networks have not adequately considered how to provide efficient inputs to the
intermediate enhancement layer. To address these issues, this paper proposes a
time-frequency (T-F) domain SE network (DPCFCS-Net) that incorporates improved
densely connected blocks, dual-path modules, convolution-augmented transformers
(conformers), channel attention, and spatial attention. Compared with previous
models, our proposed model has a more efficient encoder-decoder and can learn
comprehensive features. Experimental results on the VCTK+DEMAND dataset
demonstrate that our method outperforms existing techniques in SE performance.
Furthermore, the improved densely connected block and two dimensions attention
module developed in this work are highly adaptable and easily integrated into
existing networks.Comment: Accepted at Interspeech202
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