Acoustic models and Kalman filtering strategies for active binaural sound localization

Abstract

This paper deals with binaural sound localization. An active strategy is proposed, relying on a precise model of the dynamic changes induced by motion on the auditive perception. The proposed framework allows motions of both the sound source and the sensor. The resulting stochastic discrete-time model is then exploited together with Unscented Kalman filtering to provide range and azimuth estimation. Simulations and experiments show the effectiveness of the method.

Publication
in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems