| Paper ID | AUD-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 |
| Session | AUD-27: Acoustic Sensor Array Processing 1: Array Design and Calibration |
| Location | Gather.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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
