# Complex-Cycle-Consistent Diffusion Model for Monaural Speech Enhancement

arXiv: https://arxiv.org/abs/2412.08856 · HTML: https://mlsanity.com/paper/2412.08856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 5 of 5)

- [Improved Speech Enhancement with the Wave-U-Net](https://mlsanity.com/paper/1811.11307/) (2018)
- [Noise-aware Speech Enhancement using Diffusion Probabilistic Model](https://mlsanity.com/paper/2307.08029/) (2023)
- [SCP-GAN: Self-Correcting Discriminator Optimization for Training Consistency Preserving Metric GAN on Speech Enhancement Tasks](https://mlsanity.com/paper/2210.14474/) (2022)
- [Real-time Monaural Speech Enhancement With Short-time Discrete Cosine Transform](https://mlsanity.com/paper/2102.04629/) (2021)
- [Realistic Noise Synthesis with Diffusion Models](https://mlsanity.com/paper/2305.14022/) (2023)

## Built on by (most cited first, 1 of 1)

- [Rethinking Flow and Diffusion Bridge Models for Speech Enhancement](https://mlsanity.com/paper/2602.18355/) (2026)

## Abstract (arXiv)

In this paper, we present a novel diffusion model-based monaural speech enhancement method. Our approach incorporates the separate estimation of speech spectra's magnitude and phase in two diffusion networks. Throughout the diffusion process, noise clips from real-world noise interferences are added gradually to the clean speech spectra and a noise-aware reverse process is proposed to learn how to generate both clean speech spectra and noise spectra. Furthermore, to fully leverage the intrinsic relationship between magnitude and phase, we introduce a complex-cycle-consistent (CCC) mechanism that uses the estimated magnitude to map the phase, and vice versa. We implement this algorithm within a phase-aware speech enhancement diffusion model (SEDM). We conduct extensive experiments on public datasets to demonstrate the effectiveness of our method, highlighting the significant benefits of exploiting the intrinsic relationship between phase and magnitude information to enhance speech. The comparison to conventional diffusion models demonstrates the superiority of SEDM.