Efficient Full-Rank Spatial Covariance Estimation Using Independent Low-Rank Matrix Analysis for Blind Source Separation
Efficient Full-Rank Spatial Covariance Estimation Using Independent Low-Rank Matrix Analysis for Blind Source Separation
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DOI：
10.23919/eusipco.2019.8903026
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Yuki Kubo;Norihiro Takamune;Daichi Kitamura;H. Saruwatari
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文献类型：
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作者：
Yuki Kubo;Norihiro Takamune;Daichi Kitamura;H. Saruwatari
在本文中，我们提出了一种基于独立低秩矩阵分析（ILRMA）有效分离定向源和扩散背景噪声的新算法。 ILRMA 是最先进的盲源分离 (BSS) 技术之一，基于 -1 级空间模型。尽管这样的模型不适用于扩散噪声，但 ILRMA 可以准确估计定向源的空间参数。受此事实的启发，我们利用这些估计来恢复扩散噪声丢失的空间基础，这可以被视为有效的全秩空间协方差估计。 BSS 实验证明了该方法在计算成本和分离性能方面的有效性。
In this paper, we propose a new algorithm that efficiently separates a directional source and diffuse background noise based on independent low-rank matrix analysis (ILRMA). ILRMA is one of the state-of-the-art techniques of blind source separation (BSS) and is based on a rank -1 spatial model. Although such a model does not hold for diffuse noise, ILRMA can accurately estimate the spatial parameters of the directional source. Motivated by this fact, we utilize these estimates to restore the lost spatial basis of diffuse noise, which can be considered as an efficient full-rank spatial covariance estimation. BSS experiments show the efficacy of the proposed method in terms of the computational cost and separation performance.