Asynchronous Microphone array calibration is a prerequisite for most audition robot applications. In practice, the calibration requires estimating microphone positions, time offsets, clock drift rates, and sound event locations simultaneously. The existing method proposed Graph-based Simultaneous Localisation and Mapping (Graph-SLAM) utilizing common TDOA, time difference of arrival between two microphones (TDOA-M), and odometry measurement, however, it heavily depends on the initial value. In this paper, we propose a novel TDOA, time difference of arrival between adjacent sound events (TDOA-S), combine it with TDOA-M, called hybrid TDOA, and add odometry measurement to construct Graph-SLAM and use the Gauss-Newton (GN) method to solve. TDOA-S is simple and efficient because it eliminates time offset without generating new variables. Simulation and real-world experiment results consistently show that our method is independent of microphone number, insensitive to initial values, and has better calibration accuracy and stability 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, which explains the advantages of my method.

翻译：异步麦克风阵列标定是大多数听觉机器人应用的前提条件。实际应用中，标定需要同时估计麦克风位置、时间偏移、时钟漂移率以及声源事件位置。现有方法采用基于公共TDOA（到达时间差）的图优化同时定位与地图构建（Graph-SLAM），利用双麦克风到达时间差（TDOA-M）和里程计测量值，但该方法严重依赖初始值。本文提出一种新型相邻声源事件到达时间差（TDOA-S），并将其与TDOA-M结合称为混合TDOA，同时引入里程计测量值构建Graph-SLAM，采用高斯-牛顿（GN）法进行求解。TDOA-S简单高效，因其无需生成新变量即可消除时间偏移。仿真与真实实验结果表明，本方法不受麦克风数量限制、对初始值不敏感，并在不同TDOA噪声条件下具有更优的标定精度与稳定性。此外，仿真结果显示，本方法对麦克风参数具有更低的克拉美-罗下界（CRLB），这解释了本方法的优势所在。