  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.
