Gaussian modeling of mixtures of non-stationary signals in the Time-Frequency domain (HR-NMF)
Gaussian modeling of mixtures of non-stationary signals in the Time-Frequency domain (HR-NMF)
复制标题
时频域非平稳信号混合的高斯建模 (HR-NMF)
DOI：
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发表时间：
2011
期刊：
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通讯作者：
R. Badeau
中科院分区：
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作者：
R. Badeau
非负矩阵分解（NMF）是在时频域分解非平稳混合信号的有力工具。然而，与专用于指数混合的高分辨率（HR）方法不同，其光谱分辨率受到底层TF表示的限制。在本文中，我们提出了一个统一的概率模型，称为HR-NMF，允许克服这一限制，同时考虑相位和本地相关性，在每个频带。这个模型估计与EM算法的递归实现，这是成功地应用于源分离和音频修复。
Nonnegative Matrix Factorization (NMF) is a powerful tool for decomposing mixtures of non-stationary signals in the Time-Frequency (TF) domain. However, unlike the High Resolution (HR) methods dedicated to mixtures of exponentials, its spectral resolution is limited by that of the underlying TF representation. In this paper, we propose a unified probabilistic model called HR-NMF, that permits to overcome this limit by taking both phases and local correlations in each frequency band into account. This model is estimated with a recursive implementation of the EM algorithm, that is successfully applied to source separation and audio inpainting.