  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.
