Signal separation and extraction are important tasks for devices recording audio signals in real environments which, aside from the desired sources, often contain several interfering sources such as background noise or concurrent speakers. Blind Source Separation (BSS) provides a powerful approach to address such problems. However, BSS algorithms typically treat all sources equally and do not resolve uncertainty regarding the ordering of the separated signals at the output of the algorithm, i.e., the outer permutation problem. This paper addresses this problem by incorporating prior knowledge into the adaptation of the demixing filters, e.g., the position of the sources, in a probabilistic framework. We focus here on methods based on Independent Vector Analysis (IVA) as it elegantly and successfully deals with the internal permutation problem. By including a background model, i.e., a model for sources we are not interested to separate, we enable the algorithm to extract the sources of interest in overdetermined and underdetermined scenarios at a low computational complexity. The proposed framework allows to incorporate prior knowledge about the demixing filters in a generic way and unifies several known and newly proposed algorithms using a probabilistic view. For all algorithmic variants, we provide efficient update rules based on the iterative projection principle. The performance of a large variety of representative algorithmic variants, including very recent algorithms, is compared using measured Room Impulse Responses (RIRs).

中文翻译：

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基于独立向量分析的空间信息源分离提取统一概率观

对于在真实环境中记录音频信号的设备来说，信号分离和提取是重要的任务，除了所需的源之外，通常还包含多个干扰源，例如背景噪声或并发扬声器。盲源分离 (BSS) 提供了一种强大的方法来解决此类问题。然而，BSS 算法通常平等地对待所有源，并且不解决关于算法输出处的分离信号的排序的不确定性，即外置换问题。本文通过在概率框架中将先验知识合并到去混合滤波器的适应中来解决这个问题，例如，源的位置。我们在这里重点介绍基于独立向量分析 (IVA) 的方法，因为它优雅而成功地处理了内部置换问题。通过包括背景模型，即我们不感兴趣的源模型，我们使算法能够以低计算复杂度在超定和欠定场景中提取感兴趣的源。所提出的框架允许以通用方式合并有关解混滤波器的先验知识，并使用概率视图统一几种已知和新提出的算法。对于所有算法变体，我们提供基于迭代投影原理的高效更新规则。使用测量的房间脉冲响应 (RIR) 来比较各种代表性算法变体（包括最新算法）的性能。我们使算法能够以低计算复杂度提取超定和欠定场景中的兴趣来源。所提出的框架允许以通用方式合并有关解混滤波器的先验知识，并使用概率视图统一几种已知和新提出的算法。对于所有算法变体，我们提供基于迭代投影原理的高效更新规则。使用测量的房间脉冲响应 (RIR) 来比较各种代表性算法变体（包括最新算法）的性能。我们使算法能够以低计算复杂度提取超定和欠定场景中的兴趣来源。所提出的框架允许以通用方式合并有关解混滤波器的先验知识，并使用概率视图统一几种已知和新提出的算法。对于所有算法变体，我们提供基于迭代投影原理的高效更新规则。使用测量的房间脉冲响应 (RIR) 来比较各种代表性算法变体（包括最新算法）的性能。所提出的框架允许以通用方式合并有关解混滤波器的先验知识，并使用概率视图统一几种已知和新提出的算法。对于所有算法变体，我们提供基于迭代投影原理的高效更新规则。使用测量的房间脉冲响应 (RIR) 来比较各种代表性算法变体（包括最新算法）的性能。所提出的框架允许以通用方式合并有关解混滤波器的先验知识，并使用概率视图统一几种已知和新提出的算法。对于所有算法变体，我们提供基于迭代投影原理的高效更新规则。使用测量的房间脉冲响应 (RIR) 来比较各种代表性算法变体（包括最新算法）的性能。