Alpha-stable multichannel audio source separation
Alpha-stable multichannel audio source separation
复制标题
Alpha 稳定的多通道音频源分离
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
G. Richard
中科院分区：
文献类型：
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作者：
Simon Leglaive;Umut Simsekli;A. Liutkus;R. Badeau;G. Richard
在本文中，我们重点关注在短时傅里叶变换域中对多通道音频信号进行建模，以实现源分离。我们提出了一种基于一类重尾分布的概率模型，其中观测到的混合物和潜在源通过使用某一类多变量α稳定分布来联合建模。与传统的高斯模型相反，在传统的高斯模型中，观测值被限制在与平均值的几个标准差之内，所提出的重尾模型允许我们考虑模型中的虚假数据或重要的不确定性。我们开发了一个蒙特卡洛期望最大化算法推断的来源，从所提出的模型。我们表明，我们的方法导致显着的性能改进，在音频源分离损坏的混合物和空间音频对象编码。
In this paper, we focus on modeling multichannel audio signals in the short-time Fourier transform domain for the purpose of source separation. We propose a probabilistic model based on a class of heavy-tailed distributions, in which the observed mixtures and the latent sources are jointly modeled by using a certain class of multivariate alpha-stable distributions. As opposed to the conventional Gaussian models, where the observations are constrained to lie just within a few standard deviations from the mean, the proposed heavy-tailed model allows us to account for spurious data or important uncertainties in the model. We develop a Monte Carlo Expectation-Maximization algorithm for inferring the sources from the proposed model. We show that our approach leads to significant performance improvements in audio source separation under corrupted mixtures and in spatial audio object coding.