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arXiv · 2102.03109

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

Alexandru Nelus·Rene Glitza·Rainer Martin
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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.

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BibTeXRIS

Alexandru Nelus, Rene Glitza, Rainer Martin. 2021-02-05. Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated Learning. https://arxiv.org/abs/2102.03109

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