Sparse Adaptation of Distributed Blind Source Separation in Acoustic Sensor Networks

Günther M, Afifi H, Brendel A, Karl H, Kellermann W (2019)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2019

Event location: New Paltz

DOI: 10.1109/waspaa.2019.8937194

Abstract

By distributing the computational load over the nodes of a Wireless Acoustic Sensor Network (WASN), the real-time capability of the TRINICON (TRIple-N-Independent component analysis for CON-
volutive mixtures) framework for Blind Source Separation (BSS) can be ensured, even if the individual network nodes are not powerful enough to run TRINICON in real-time by themselves. To optimally utilize the limited computing power and data rate in WASNs, the MARVELO (Multicast-Aware Routing for Virtual network Embedding with Loops in Overlays) framework is expanded for use with TRINICON, while a feature-based selection scheme is proposed to exploit the most beneficial parts of the input signal for adapting the demixing system. The simulation results of realistic scenarios show only a minor degradation of the separation performance even in heavily resource-limited situations.

Authors with CRIS profile

Michael Günther Professur für Signalverarbeitung Andreas Brendel Professur für Signalverarbeitung Walter Kellermann Professur für Signalverarbeitung

Involved external institutions

Universität Paderborn DE Germany (DE)

How to cite

APA:

Günther, M., Afifi, H., Brendel, A., Karl, H., & Kellermann, W. (2019). Sparse Adaptation of Distributed Blind Source Separation in Acoustic Sensor Networks. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). New Paltz.

MLA:

Günther, Michael, et al. "Sparse Adaptation of Distributed Blind Source Separation in Acoustic Sensor Networks." Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz 2019.

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