Variational Bayesian EM algorithm for modeling mixtures of non-stationary signals in the time-frequency domain (HR-NMF)
Variational Bayesian EM algorithm for modeling mixtures of non-stationary signals in the time-frequency domain (HR-NMF)
复制标题
用于对时频域中的非平稳信号混合进行建模的变分贝叶斯 EM 算法 (HR-NMF)
DOI：
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发表时间：
2013
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通讯作者：
Angélique Dremeau
中科院分区：
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作者：
R. Badeau;Angélique Dremeau
最近，我们引入了高分辨率非负矩阵分解（HR-NMF）模型，用于分析时频域中的非平稳信号的混合，并强调了其同时达到高频谱分辨率和重构高质量音频信号的能力。为了估计模型参数和潜在成分，我们提出了诉诸于期望最大化（EM）算法的基础上卡尔曼滤波器/平滑。该方法被证明是适当的建模音频信号的应用，如源分离和音频修复。然而，它的计算成本很高，主要由卡尔曼滤波器/平滑器，并且在处理高维信号时可能是禁止的。在本文中，我们考虑两种不同的替代方案，使用变分贝叶斯EM算法和两个平均场近似。我们表明，虽然显着降低了估计的复杂性，这些新的方法不会改变其质量。
We recently introduced the high-resolution nonnegative matrix factorization (HR-NMF) model for analyzing mixtures of nonstationary signals in the time-frequency domain, and highlighted its capability to both reach high spectral resolution and reconstruct high quality audio signals. In order to estimate the model parameters and the latent components, we proposed to resort to an expectation-maximization (EM) algorithm based on a Kalman filter/smoother. The approach proved to be appropriate for modeling audio signals in applications such as source separation and audio inpainting. However, its computational cost is high, dominated by the Kalman filter/smoother, and may be prohibitive when dealing with high-dimensional signals. In this paper, we consider two different alternatives, using the variational Bayesian EM algorithm and two mean-field approximations. We show that, while significantly reducing the complexity of the estimation, these novel approaches do not alter its quality.