Auxiliary-function-based independent vector analysis with power of vector-norm type weighting functions
Auxiliary-function-based independent vector analysis with power of vector-norm type weighting functions
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发表时间：
2012-12
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通讯作者：
Nobutaka Ono
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作者：
Nobutaka Ono
本文在广义超高斯源模型或时变方差高斯源模型的基础上，提出了一种基于辅助函数的独立向量分析方法。AuxIVA是一种具有球形超高斯源模型的独立向量分析（IVA）迭代算法，源模型可以用加权函数来表征。我们证明了形状参数为0 <；β≤2和时变方差的高斯源模型统一得到幂次向量范数型加权函数。讨论了数值稳定性的标度和裁剪技术。研究了分离性能对源模型的依赖关系。
In this paper, we present an auxiliary-function-based independent vector analysis (AuxIVA) based on the Generalized super Gaussian source model or Gaussian source model with time-varying variance. AuxIVA is a convergence-guaranteed iterative algorithm for independent vector analysis (IVA) with a spherical and super Gaussian source model, and the source model can be characterized by a weighting function. We show that both of the generalized Gaussian source models with the shape parameter 0 <; β ≤ 2 and the Gaussian source model with time-varying variance unifiedly yield a power of vector-norm type weighting functions. A scaling and a clipping technique for numerical stability are discussed. The dependency of the separation performance on the source model is also investigated.