Asynchronous microphone array calibration is a prerequisite for many audition robot applications. A popular solution to the above calibration problem is the batch form of Simultaneous Localisation and Mapping (SLAM), using the time difference of arrival measurements between two microphones (TDOA-M), and the robot (which serves as a moving sound source during calibration) odometry information. In this paper, we introduce a new form of measurement for microphone array calibration, i.e. the time difference of arrival between adjacent sound events (TDOA-S) with respect to the microphone channels. We propose to use TDOA-S and TDOA-M, called hybrid TDOA, together with odometry measurements for bath SLAM-based calibration of asynchronous microphone arrays. Extensive simulation and real-world experiments show that our method is more independent of microphone number, less sensitive to initial values (when using off-the-shelf algorithms such as Gauss-Newton iterations), and has better calibration accuracy and robustness under various TDOA noises. Simulation results also demonstrate that our method has a lower Cram\'er-Rao lower bound (CRLB) for microphone parameters. To benefit the community, we open-source our code and data at https://github.com/AISLAB-sustech/Hybrid-TDOA-Calib.

翻译：异步麦克风阵列标定是众多听觉机器人应用的前提条件。针对上述标定问题，一种常用解决方案是采用批处理形式的同步定位与建图（SLAM）方法，该方法利用两个麦克风间的到达时间差测量值（TDOA-M）以及机器人（在校准过程中作为移动声源）的里程计信息。本文为麦克风阵列标定引入了一种新型测量形式，即相邻声事件在麦克风通道间的到达时间差（TDOA-S）。我们提出将TDOA-S与TDOA-M（合称为混合TDOA）与里程计测量值相结合，用于基于批处理SLAM的异步麦克风阵列标定。大量仿真与真实场景实验表明，我们的方法对麦克风数量的依赖性更低，对初始值更不敏感（当使用高斯-牛顿迭代等现成算法时），且在各种TDOA噪声环境下具有更优的标定精度与鲁棒性。仿真结果同时证明，我们的方法对麦克风参数具有更低的克拉美-罗下界（CRLB）。为促进学术共享，我们在 https://github.com/AISLAB-sustech/Hybrid-TDOA-Calib 开源了代码与数据。