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。

扩散高斯混合音频降噪

Diffusion Gaussian Mixture Audio Denoise

Aggregating signals from a collection of noisy sources is a fundamental
problem in many domains including crowd-sourcing, multi-agent planning, sensor
networks, signal processing, voting, ensemble learning, and federated learning.
The core question is how to aggregate signals from multiple sources (e.g.
experts) in order to reveal an underlying ground truth. While a full answer
depends on the type of signal, correlation of signals, and desired output, a
problem common to all of these applications is that of differentiating sources
based on their quality and weighting them accordingly. It is often assumed that
this differentiation and aggregation is done by a single, accurate central
mechanism or agent (e.g. judge). We complicate this model in two ways. First,
we investigate the setting with both a single judge, and one with multiple
judges. Second, given this multi-agent interaction of judges, we investigate
various constraints on the judges' reporting space. We build on known results
for the optimal weighting of experts and prove that an ensemble of sub-optimal
mechanisms can perform optimally under certain conditions. We then show
empirically that the ensemble approximates the performance of the optimal
mechanism under a broader range of conditions.

研究了多个质量不同的信号来源的信号聚合问题，旨在通过不同质量来源的离散报告，构建一个公正权重的最优聚合模型。