Learning-based acoustic source localization in acoustic sensor networks using the coherent-to-diffuse power ratio

Conference contribution


Publication Details

Author(s): Brendel A, Kellermann W
Publication year: 2018
Pages range: 1586-1590
ISBN: 978-90-827970-1-5
Language: English


Abstract

A distributed learning-based algorithm for the localization of acoustic sources in an acoustic sensor network is proposed. It is based on estimates of the Coherent-to-Diffuse Power Ratio (CDR), which serve as feature for the source-microphone distance, i.e., the range. The relation between the estimated CDR and the range is learned by using Gaussian processes for non-parametric regression. The range estimates obtained from evaluating the regression function are fused by a weighted least squares estimation, which is implemented recursively, allowing for a distributed version of the algorithm. The resulting method is computationally efficient, works in highly reverberant and noisy scenarios and needs only a small amount of data shared over the network. The training phase of the algorithm requires only a few labeled observations. We show the efficacy of the approach with data obtained from image-source simulation.


FAU Authors / FAU Editors

Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik


How to cite

APA:
Brendel, A., & Kellermann, W. (2018). Learning-based acoustic source localization in acoustic sensor networks using the coherent-to-diffuse power ratio. In Proceedings of the European Signal Processing Conference (EUSIPCO) (pp. 1586-1590). Rome, IT.

MLA:
Brendel, Andreas, and Walter Kellermann. "Learning-based acoustic source localization in acoustic sensor networks using the coherent-to-diffuse power ratio." Proceedings of the European Signal Processing Conference (EUSIPCO), Rome 2018. 1586-1590.

BibTeX: 

Last updated on 2019-03-06 at 07:11