Distance estimation of acoustic sources using the coherent-to-diffuse power ratio based on distributed training

Conference contribution


Publication Details

Author(s): Brendel A, Kellermann W
Publication year: 2018
Pages range: 246-250
ISBN: 978-1-5386-8151-0
Language: English


Abstract

A range estimation method relying on distributed training in an Acoustic Sensor Network (ASN) is proposed. The relation between the estimated Coherent-to-Diffuse Power Ratio (CDR) which is used as feature and the range of an acoustic source is learned by Gaussian
Process (GP) regression. To this end multiple sensor nodes, each equipped with two microphones are distributed over the area of interest delivering multiple observations of the feature and extending the amount of training data significantly compared to the single node
case. However, the computational power of the sensor nodes in an ASN is usually limited and a transmission of the data to a fusion center is infeasible due to constraints of the transmit power and because such a system would fail if transmission drop outs occur or the fusion center breaks. Hence, we aim at a completely distributed algorithm which is as exact as a corresponding centralized version, computationally simple and update-based, i.e., all sensor nodes have to fulfill exactly the same role in the algorithm. The efficacy of the proposed method is shown by a simulation study.


FAU Authors / FAU Editors

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


How to cite

APA:
Brendel, A., & Kellermann, W. (2018). Distance estimation of acoustic sources using the coherent-to-diffuse power ratio based on distributed training. (pp. 246-250). Tokyo, JP.

MLA:
Brendel, Andreas, and Walter Kellermann. "Distance estimation of acoustic sources using the coherent-to-diffuse power ratio based on distributed training." Proceedings of the International Workshop on Acoustic Signal Enhancement (IWAENC), Tokyo 2018. 246-250.

BibTeX: 

Last updated on 2018-16-10 at 10:53