Ad hoc acoustic networks comprising multiple nodes, each of which consists of several microphones, are addressed. From the ad hoc nature of the node constellation, microphone positions are unknown. Hence, typical tasks, such as localization, tracking, and beamforming, cannot be directly applied. To tackle this challenging joint multiple speaker localization and array calibration task, we propose a novel variant of the expectation-maximization (EM) algorithm. The coordinates of multiple arrays relative to an anchor array are blindly estimated using naturally uttered speech signals of multiple concurrent speakers. The speakers’ locations, relative to the anchor array, are also estimated. The inter-distances of the microphones in each array, as well their orientations, are assumed known, which is a reasonable assumption for many modern mobile devices (in outdoor and in a several indoor scenarios). The well-known initialization problem of the batch EM algorithm is circumvented by an incremental procedure, also derived here. The proposed algorithm is tested by an extensive simulation study.

中文翻译：

---

使用期望最大化的联合扬声器定位和阵列校准

解决了包含多个节点的自组织声学网络，每个节点由多个麦克风组成。从节点星座的特殊性质来看，麦克风位置是未知的。因此，不能直接应用典型的任务，例如定位、跟踪和波束成形。为了解决这一具有挑战性的联合多扬声器定位和阵列校准任务，我们提出了一种期望最大化 (EM) 算法的新变体。使用多个并发说话者自然发出的语音信号盲目估计多个阵列相对于一个锚点阵列的坐标。扬声器相对于锚阵列的位置也被估计。假设每个阵列中麦克风的间距以及它们的方向是已知的，这是许多现代移动设备（在室外和几个室内场景中）的合理假设。批量 EM 算法的众所周知的初始化问题是通过增量过程规避的，这里也推导出来。所提出的算法通过广泛的模拟研究进行了测试。