NADiffuSE: Noise-aware Diffusion-based Model for Speech Enhancement
By Wen Wang, Dongchao Yang, Qichen Ye, Bowen Cao, Yuexian Zou. 2023
6 citations recorded by the source corpus.
The goal of speech enhancement (SE) is to eliminate the background interference from the noisy speech signal. Generative models such as diffusion models (DM) have been applied to the task of SE because of better generalization in unseen noisy scenes. Technical routes for the DM-based SE methods can be summarized into three types: task-adapted diffusion process formulation, generator-plus-conditioner (GPC) structures and the multi-stage frameworks. We focus on the first two approaches, which are constructed under the GPC architecture and use the task-adapted diffusion process to better deal with the real noise. However, the performance of these SE models is limited by the following issues: (a) Non-Gaussian noise estimation in the task-adapted diffusion process. (b) Conditional domain bias caused by the weak conditioner design in the GPC structure. (c) Large amount of residual noise caused by unreasonable interpolation operations during inference. To solve the above problems, we propose a noise-aware diffusion-based SE model (NADiffuSE) to boost the SE performance, where the noise representation is extracted from the noisy speech signal and introduced as a global conditional information for estimating the non-Gaussian components. Furthermore, the anchor-based inference algorithm is employed to achieve a compromise between the speech distortion and noise residual. In order to mitigate the performance degradation caused by the conditional domain bias in the GPC framework, we investigate three model variants, all of which can be viewed as multi-stage SE based on the preprocessing networks for Mel spectrograms. Experimental results show that NADiffuSE outperforms other DM-based SE models under the GPC infrastructure. Audio samples are available at: https://square-of-w.github.io/NADiffuSE-demo/.
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- Dongchao Yang
- Rongjie Huang
- Songxiang Liu
- Jinchuan Tian
- Xuejiao Tan
- Xuankai Chang
- Jiang Bian
- Jiatong Shi
Citations in the corpus
- UniAudio: An Audio Foundation Model Toward Universal Audio Generation (2023, methodology): “Following Wang et al. (2023a); Kharitonov et al. (2023), neural codec models (Défossez et al., 2022; Yang et al., 2023b) are used in this work for audio tokenization.”