2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-27.5
Paper Title ESTIMATION OF MICROPHONE CLUSTERS IN ACOUSTIC SENSOR NETWORKS USING UNSUPERVISED FEDERATED LEARNING
Authors Alexandru Nelus, Rene Glitza, Rainer Martin, Institute of Communication Acoustics, Ruhr University Bochum, Germany
SessionAUD-27: Acoustic Sensor Array Processing 1: Array Design and Calibration
LocationGather.Town
Session Time:Friday, 11 June, 11:30 - 12:15
Presentation Time:Friday, 11 June, 11:30 - 12:15
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-ASAP] Acoustic Sensor Array Processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper we present a privacy-aware method for estimating source-dominated microphone clusters in the context of acoustic sensor networks (ASNs). The approach is based on clustered federated learning which we adapt to unsupervised scenarios by employing a light-weight autoencoder model. The model is further optimized for training on very scarce data. In order to best harness the benefits of clustered microphone nodes in ASN applications, a method for the computation of cluster membership values is introduced. We validate the performance of the proposed approach using distance-based criteria and a network-wide classification task.