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arXiv · 2310.19602

DCHT: Deep Complex Hybrid Transformer for Speech Enhancement

Jialu Li·Junhui Li·Pu Wang·Youshan Zhang
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Abstract

Most of the current deep learning-based approaches for speech enhancement only operate in the spectrogram or waveform domain. Although a cross-domain transformer combining waveform- and spectrogram-domain inputs has been proposed, its performance can be further improved. In this paper, we present a novel deep complex hybrid transformer that integrates both spectrogram and waveform domains approaches to improve the performance of speech enhancement. The proposed model consists of two parts: a complex Swin-Unet in the spectrogram domain and a dual-path transformer network (DPTnet) in the waveform domain. We first construct a complex Swin-Unet network in the spectrogram domain and perform speech enhancement in the complex audio spectrum. We then introduce improved DPT by adding memory-compressed attention. Our model is capable of learning multi-domain features to reduce existing noise on different domains in a complementary way. The experimental results on the BirdSoundsDenoising dataset and the VCTK+DEMAND dataset indicate that our method can achieve better performance compared to state-of-the-art methods.

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Jialu Li, Junhui Li, Pu Wang, Youshan Zhang. 2023-10-30. DCHT: Deep Complex Hybrid Transformer for Speech Enhancement. https://arxiv.org/abs/2310.19602

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