posted on 2024-04-23, 10:15authored byTimm 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
History
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"
}