  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.
