Estimation Of Microphone Clusters In Acoustic Sensor Networks Using Unsupervised Federated Learning
Alexandru Nelus, Rene Glitza, Rainer Martin
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SPS
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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 distance-based criteria and a network-wide classification task.
Chairs:
Jesper Rindom Jensen