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Electrical Engineering and Systems Science > Audio and Speech Processing
Title: Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated Learning
(Submitted on 5 Feb 2021 (v1), last revised 15 Feb 2021 (this version, v2))
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.
Submission history
From: Alexandru Nelus M. Sc. [view email][v1] Fri, 5 Feb 2021 11:21:16 GMT (733kb,D)
[v2] Mon, 15 Feb 2021 19:55:50 GMT (256kb,D)
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