Recent diffusion models have achieved promising performances in
audio-denoising tasks. The unique property of the reverse process could recover
clean signals. However, the distribution of real-world noises does not comply
with a single Gaussian distribution and is even unknown. The sampling of
Gaussian noise conditions limits its application scenarios. To overcome these
challenges, we propose a DiffGMM model, a denoising model based on the
diffusion and Gaussian mixture models. We employ the reverse process to
estimate parameters for the Gaussian mixture model. Given a noisy audio signal,
we first apply a 1D-U-Net to extract features and train linear layers to
estimate parameters for the Gaussian mixture model, and we approximate the real
noise distributions. The noisy signal is continuously subtracted from the
estimated noise to output clean audio signals. Extensive experimental results
demonstrate that the proposed DiffGMM model achieves state-of-the-art
performance.

提出了一种 DiffGMM 模型，它是基于扩散和高斯混合模型的去噪模型。通过反向过程估计高斯混合模型的参数，从而近似真实噪声分布，并通过连续减去估计的噪声来输出清晰的音频信号。广泛的实验结果表明，所提出的 DiffGMM 模型达到了最先进的性能。