Permutation-Free Cgmm: Complex Gaussian Mixture Model with Inverse Wishart Mixture Model Based Spatial Prior for Permutation-Free Source Separation and Source Counting
Permutation-Free Cgmm: Complex Gaussian Mixture Model with Inverse Wishart Mixture Model Based Spatial Prior for Permutation-Free Source Separation and Source Counting
复制标题
无置换 Cgmm：具有基于逆 Wishart 混合模型的空间先验的复杂高斯混合模型，用于无置换源分离和源计数
DOI：
10.1109/icassp.2018.8461934
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
T. Nakatani
中科院分区：
文献类型：
--
作者：
Juan Azcarreta;N. Ito;S. Araki;T. Nakatani
在这里，我们提出了一个无置换的cGMM（PF-cGMM），一个新的概率模型的观察到的混合物，它可以解决频率箱之间的置换模糊性，并且是适用的，即使当源的数量是未知的。最近提出的复高斯混合模型（cGMM）是非常有效的频率箱式聚类时，源的数量是已知的。然而，它不能解决排列歧义，并且不适用于源的数量是未知的。所提出的PF-cGMM是cGMM的扩展，其解决了这些问题。置换模糊度的解决可以通过称为复逆Wishart混合模型（cIWMM）的空间先验来实现。排列模糊的情况下，有利于源计数，这是通过层次聚类在本文中执行。实验结果表明，PF-cGMM算法能够（1）解决置换模糊问题，（2）即使在信源个数未知的情况下也能实现信源分离，且与已知信源个数时相比，性能下降很小。
Here we propose a permutation-free cGMM (PF-cGMM), a new probabilistic model of observed mixtures, which can resolve permutation ambiguity between frequency bins, and is applicable even when the number of sources is unknown. A recently proposed complex Gaussian mixture model (cGMM) is highly effective for frequency bin-wise clustering when the number of sources is known. However, it cannot resolve the permutation ambiguity, and is inapplicable when the number of sources is unknown. The proposed PF-cGMM is an extension of the cGMM, which resolves these issues. The resolution of the permutation ambiguity can be realized by a spatial prior called a complex inverse Wishart mixture model (cIWMM). The absence of the permutation ambiguity facilitates source counting, which is performed by hierarchical clustering in this paper. Experiments showed that the PF-cGMM was able to (1) resolve the permutation ambiguity and (2) realize source separation even when the number of sources was unknown with little performance degradation compared to when it was known.