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

2021/02/05 by Nelus, Alexandru, Glitza, Rene, Martin, Rainer · 1 citation
#Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Sound (cs.SD) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2102.03109

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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• FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification

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