Position and Trajector Learning for Microphone Arrays

In this paper we tackle the problem of source localization by example. We present a methodology that allows a user to train a microphone array system using signals from a set of positions and trajectories and subsequently recall the localization information when presented with new input signals. To do so we present a new statistical model which is capable of accurately describing features from the cross spectra of the microphone signals so as to model the room responses from all positions of interest. We further extend this model to allow modeling of sequences of positions, thereby also enabling the learning and recognition of trajectories. Because of its learning nature this method provides practical advantages in setting up a microphone array, by not requiring favorable room acoustics, careful element positioning or uniformity of sensors. It also introduces an approach to localization which can be extended to other problems requiring models of transfer functions. We present tests on synthetic and real-world data and present the resulting recognition rates for a variety of situations. IEEE Transactions on Audio, Speech and Language Processing This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2007 201 Broadway, Cambridge, Massachusetts 02139