Estimation of Microphone Clusters in Acoustic Sensor Networks Using Unsupervised Federated Learning

Alexandru Nelus; Rene Glitza; Rainer Martin

ICASSP 2021accepted0 citations

Estimation of Microphone Clusters in Acoustic Sensor Networks Using Unsupervised Federated Learning

Alexandru Nelus, Rene Glitza, Rainer Martin

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 clustering-based measures and a network-wide classification task.

BibTeX

@inproceedings{icassp2021_estimationofmicr,
  title = {Estimation of Microphone Clusters in Acoustic Sensor Networks Using Unsupervised Federated Learning},
  author = {Alexandru Nelus and Rene Glitza and Rainer Martin},
  booktitle = {ICASSP 2021},
  year = {2021}
}