Underdetermined Convolutive Blind Source Separation via Time-Frequency Masking
Underdetermined Convolutive Blind Source Separation via Time-Frequency Masking
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DOI：
10.1109/tasl.2009.2024380
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发表时间：
2010-01-01
影响因子：
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通讯作者：
Soon, Ing Yann
中科院分区：
文献类型：
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作者：
Reju, Vaninirappuputhenpurayil Gopalan;Koh, Soo Ngee;Soon, Ing Yann
在本文中，我们考虑通过时频（TF）掩蔽将未知数量的源从其不确定的卷积混合物中分离出来的问题。我们提出了两种算法，一种用于估计将应用于 TF 域中的混合的掩模，以在频域中分离信号，另一种用于解决排列问题。掩模估计算法基于复向量空间中的角度概念。与之前报道的方法不同，该算法不需要对混合矩阵或掩模估计的源位置进行任何估计。该算法使用众所周知的 k 均值或模糊 c 均值聚类算法，根据样本向量和参考向量之间的埃尔米特角在 TF 域中对混合样本进行聚类。从聚类算法中获得的隶属函数直接用作掩模。用于解决排列问题的算法通过使用具有重叠的小组附近掩模的 k 均值聚类来对估计掩模进行聚类。证明了该算法将源（包括共线源）与在真实房间环境中获得的欠定卷积混合物分离的有效性。
In this paper, we consider the problem of separation of unknown number of sources from their underdetermined convolutive mixtures via time-frequency (TF) masking. We propose two algorithms, one for the estimation of the masks which are to be applied to the mixture in the TF domain for the separation of signals in the frequency domain, and the other for solving the permutation problem. The algorithm for mask estimation is based on the concept of angles in complex vector space. Unlike the previously reported methods, the algorithm does not require any estimation of the mixing matrix or the source positions for mask estimation. The algorithm clusters the mixture samples in the TF domain based on the Hermitian angle between the sample vector and a reference vector using the well known k-means or fuzzy c-means clustering algorithms. The membership functions so obtained from the clustering algorithms are directly used as the masks. The algorithm for solving the permutation problem clusters the estimated masks by using k-means clustering of small groups of nearby masks with overlap. The effectiveness of the algorithm in separating the sources, including collinear sources, from their underdetermined convolutive mixtures obtained in a real room environment, is demonstrated.