Independent Vector Analysis (IVA) is a popular extension of Independent Component Analysis (ICA) for joint separation of a set of instantaneous linear mixtures, with a direct application in frequency-domain speaker separation or extraction. The mixtures are parameterized by mixing matrices, one matrix per mixture. This means that the IVA mixing model does not account for any relationships between parameters across the mixtures/frequencies. The separation proceeds jointly only through the source model, where statistical dependencies of sources across the mixtures are taken into account. In this paper, we propose a mixing model for joint blind source extraction where the mixing model parameters are linked across the frequencies. This is achieved by constraining the set of feasible parameters to the manifold of half-length separating filters, which has a clear interpretation and application in frequency-domain speaker extraction.

翻译：独立向量分析(IVA)是独立分量分析(ICA)的一种流行扩展，用于联合分离一组瞬时线性混合信号，在频域说话人分离或提取中具有直接应用。混合过程由混合矩阵参数化，每个混合对应一个矩阵。这意味着IVA混合模型不考虑跨混合/频率的参数间任何关系。分离仅通过源模型联合进行，其中考虑了跨混合的源统计依赖关系。本文提出一种联合盲源提取的混合模型，其中混合模型参数跨频率存在关联。这是通过将可行参数集约束在半长分离滤波器流形上实现的，该流形在频域说话人提取中具有明确的解释和应用。