Diffusion-based Speech Enhancement with Schr\"odinger Bridge and Symmetric Noise Schedule
<|reference_start|>Diffusion-based Speech Enhancement with Schr\"odinger Bridge and Symmetric Noise Schedule: Recently, diffusion-based generative models have demonstrated remarkable performance in speech enhancement tasks. However, these methods still encounter challenges, including the lack of structural information and poor performance in low Signal-to-Noise Ratio (SNR) scenarios. To overcome these challenges, we propose the Schr\"oodinger Bridge-based Speech Enhancement (SBSE) method, which learns the diffusion processes directly between the noisy input and the clean distribution, unlike conventional diffusion-based speech enhancement systems that learn data to Gaussian distributions. To enhance performance in extremely noisy conditions, we introduce a two-stage system incorporating ratio mask information into the diffusion-based generative model. Our experimental results show that our proposed SBSE method outperforms all the baseline models and achieves state-of-the-art performance, especially in low SNR conditions. Importantly, only a few inference steps are required to achieve the best result.<|reference_end|>
@article{wang2024diffusion-based,
    title={Diffusion-based Speech Enhancement with Schr\"odinger Bridge and
  Symmetric Noise Schedule},
    author={Siyi Wang, Siyi Liu, Andrew Harper, Paul Kendrick, Mathieu Salzmann,
  Milos Cernak},
    journal={arXiv preprint arXiv:2409.05116},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05116},
    primaryClass={eess.AS cs.SD}
}