Moving sound source localization and tracking for an autonomous robot equipped with a self-rotating bi-microphone array.

In this paper, we present two approaches to localizing and tracking a sound source that moves in a three-dimensional (3D) space. The sound signal was captured by a unique bi-microphone system that rotates at a constant angular velocity. The motion of the sound source along with the rotation of the bi-microphone array produces a sinusoidal inter-channel distance difference (ICDD) signal with time-varying amplitude and phase. Four state-space models were developed and employed to design extended Kalman filters (EKFs) that identify instantaneous amplitude and phase of the ICDD signal. Both theoretical and numerical observability analyses of the four state-space models were performed to reveal singularities of the proposed EKFs in the domain of interest. We also developed a Hilbert-transform based method that localizes the sound source by comparing the true analytic ICDD signal to a virtual reference signal with zero elevation and azimuth angles. A moving average filter is then applied to reduce the noise and the effect of the artifacts at the beginning and the ending portions of the estimates. The effectiveness of the proposed methods was evaluated using comparison studies in simulation.