Predictive Acoustic Tracking with an Adaptive Neural Mechanism

Tracking an acoustic signal in motion is pertinent in several domains such as human-robot interaction and search-and-rescue robotics. Conventional approaches to acoustic tracking acquire time-of-arrival-difference signals from multi-microphone arrays and localise the acoustic signal using Kalman or particle filtering, generalised cross-correlation or steered response power techniques. The authors have previously developed a biologically-inspired mechanism that utilises two microphones to reactively track an acoustic signal in motion. The mechanism leverages the directional response of an mathematical model of the lizard peripheral auditory system to extract information regarding sound direction. This information is utilised by a neural machinery to learn the acoustic signal's velocity through fast and unsupervised correlation-based learning adapted from differential Hebbian learning. This approach has previously been validated in simulation and via robotic trials to track a continuous pure tone acoustic signal with a semi-circular motion trajectory and a constant but unknown angular velocity. The neural machinery has been shown to be able to learn different target angular velocities in independent trials. Here we extend our previous work by demonstrating that an identical instance of the mechanism can be used to successfully predict the future spatial location of an acoustic signal with an identical semi-circular motion trajectory and a constant but unknown angular velocity. We evaluate the prediction performance of the simulated mechanism in independent trials for three different angular velocities.