# DCHT: Deep Complex Hybrid Transformer for Speech Enhancement

arXiv: https://arxiv.org/abs/2310.19602 · HTML: https://mlsanity.com/paper/2310.19602/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2023.

## Built on by (most cited first, 3 of 3)

- [ProSE: Diffusion Priors for Speech Enhancement](https://mlsanity.com/paper/2503.06375/) (2025)
- [Diffusion Gaussian Mixture Audio Denoise](https://mlsanity.com/paper/2406.09154/) (2024)
- [Complex Image-Generative Diffusion Transformer for Audio Denoising](https://mlsanity.com/paper/2406.09161/) (2024)

## Abstract (arXiv)

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.