Asynchronous microphone array calibration is a prerequisite for most 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 combine TDOA-S and TDOA-M, called hybrid TDOA, together with odometry measurements for bath SLAM-based calibration of asynchronous microphone arrays. Simulation and real-world experiment results consistently 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. In addition, the simulation result demonstrates 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/zcj808/Hybrid-TDOA-Calib.

翻译：异步麦克风阵列校正是大多数听觉机器人应用的前提条件。解决上述校准问题的一种常用方法是采用批处理形式的同步定位与地图构建（SLAM），利用两个麦克风之间的到达时间差测量（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。本文引入了一种新的麦克风阵列校准测量形式，即相邻声事件相对于麦克风通道的到达时间差（TDOA-S）。我们提出将TDOA-S与TDOA-M相结合（称为混合TDOA），并与里程计测量共同用于基于批处理SLAM的异步麦克风阵列校准。仿真与真实实验结果表明，我们的方法对麦克风数量的依赖性更低，对初始值的敏感度更小（当采用高斯-牛顿迭代等现成算法时），并且在各种TDOA噪声条件下具有更好的校准精度和鲁棒性。此外，仿真结果证明，我们的方法在麦克风参数上具有更低的克拉美-罗下界（CRLB）。为促进学术社区发展，我们在https://github.com/zcj808/Hybrid-TDOA-Calib开源了代码与数据。