Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated Learning
This work addresses the problem of privacy-aware microphone clustering for acoustic sensor network applications, offering an incremental improvement to existing federated learning techniques.
This paper proposes a privacy-aware, unsupervised federated learning method for estimating source-dominated microphone clusters in acoustic sensor networks. It uses a lightweight autoencoder optimized for scarce data and introduces a method for computing cluster membership values.
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.