There are many intelligent devices around us that have the ability to emit and receive audio signals, such as smartphones, smartwatches, and tablets. If they constitute an acoustic transceiver network, it can boost the performance of many audio processing tasks. The speaker localization and tracking algorithms using such a network require the prior information of node positions. To acquire this knowledge, we derive an analytical solution for geometry calibration of acoustic transceiver networks where each node consists of a microphone array and a loudspeaker. Specifically, a linear cost function for node orientations is first established using the delivered direction-of-arrival (DoA) measurements, and its analytical solution is derived. Next, based on the DoA and time-of-arrival (ToA) measurements, another cost function for node positions is formulated, which can also be solved in the closed form. Experimental results show that the proposed method can successfully estimate the geometry structure of acoustic transceiver networks in noisy and reverberant environments. In contrast to most state-of-the-art geometry calibration algorithms, which iteratively solve the calibration problem by optimization methods, the proposed method can decrease the computational complexity greatly.

中文翻译：

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声学收发器阵列的解析几何校准


我们周围有许多智能设备能够发出和接收音频信号，例如智能手机、智能手表和平板电脑。如果它们构成一个声学收发器网络，它可以提高许多音频处理任务的性能。使用这种网络的说话者定位和跟踪算法需要节点位置的先验信息。为了获得这些知识，我们得出了声学收发器网络几何校准的解析解，其中每个节点都由麦克风阵列和扬声器组成。具体来说，首先使用提供的到达方向（DoA）测量建立节点方向的线性成本函数，并导出其解析解。接下来，基于 DoA 和到达时间 (ToA) 测量，制定节点位置的另一个成本函数，该函数也可以以封闭形式求解。实验结果表明，该方法可以成功估计噪声和混响环境中声学收发器网络的几何结构。与大多数通过优化方法迭代解决校准问题的最先进的几何校准算法相比，所提出的方法可以大大降低计算复杂度。