In this paper, we propose a novel algorithm for blind source extraction (BSE) of a moving acoustic source recorded by multiple microphones. The algorithm is based on independent vector extraction (IVE) where the contrast function is optimized using the auxiliary function-based technique and where the recently proposed constant separating vector (CSV) mixing model is assumed. CSV allows for movements of the extracted source within the analyzed batch of recordings. We provide a practical explanation of how the CSV model works when extracting a moving acoustic source. Then, the proposed algorithm is experimentally verified on the task of blind extraction of a moving speaker. The algorithm is compared with state-of-the-art blind methods and with an adaptive BSE algorithm which processes data in a sequential manner. The results confirm that the proposed algorithm can extract the moving speaker better than the BSE methods based on the conventional mixing model and that it achieves improved extraction accuracy than the adaptive method.

中文翻译：

---

基于辅助函数的运动说话人盲提取算法

在本文中，我们提出了一种新算法，用于对多个麦克风记录的移动声源进行盲源提取 (BSE)。该算法基于独立向量提取 (IVE)，其中使用基于辅助函数的技术优化对比度函数，并假设最近提出的恒定分离向量 (CSV) 混合模型。CSV 允许在分析的录音批次中移动提取的源。我们提供了 CSV 模型在提取移动声源时如何工作的实用解释。然后，所提出的算法在运动说话人的盲提取任务上进行了实验验证。该算法与最先进的盲法和以顺序方式处理数据的自适应 BSE 算法进行了比较。