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Clustering-based Wake Word Detection in Privacy-aware Acoustic Sensor Networks

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conference contribution
posted on 2024-04-23, 10:15 authored by Timm Koppelmann, Luca Becker, Alexandru Nelus, Rene Glitza, Lea SchönherrLea Schönherr, Rainer Martin
This work investigates privacy-aware collaborative wake word detection (WWD) in acoustic sensor networks. To meet state-of-the-art privacy constraints, the proposed WWD scheme is based on privacy-aware unsupervised clustered federated learning that groups microphone nodes w.r.t. active sound sources and on a privacy-preserving high-level feature representation. Using the partition of microphone nodes into clusters, we apply intra- and inter-cluster feature enhancement strategies directly in the privacy-preserving feature domain and thus circumvent the need for communicating privacy-sensitive information between nodes. The approach is demonstrated for an acoustic sensor network deployed in a smart-home environment. We show that the proposed collaborative WWD system clearly outperforms independent decisions of individual microphone nodes. Index Terms: privacy, wake word detection, clustering, federated learning, unsupervised clustered federated learning

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Editor

Ko H ; Hansen JHL

Primary Research Area

  • Threat Detection and Defenses

Name of Conference

INTERSPEECH (ISCA)

Journal

INTERSPEECH

Page Range

719-723

Publisher

International Speech Communication Association

Open Access Type

  • Not Open Access

BibTeX

@conference{Koppelmann:Becker:Nelus:Glitza:Schönherr:Martin:2022, title = "Clustering-based Wake Word Detection in Privacy-aware Acoustic Sensor Networks", author = "Koppelmann, Timm" AND "Becker, Luca" AND "Nelus, Alexandru" AND "Glitza, Rene" AND "Schönherr, Lea" AND "Martin, Rainer", editor = "Ko, Hanseok" AND "Hansen, John HL", year = 2022, month = 8, journal = "INTERSPEECH", pages = "719--723", publisher = "International Speech Communication Association", doi = "10.21437/interspeech.2022-842" }

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