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

arXiv: https://arxiv.org/abs/2102.03109 · HTML: https://mlsanity.com/paper/2102.03109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2021.

## Built on by (most cited first, 1 of 1)

- [Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments](https://mlsanity.com/paper/2106.03671/) (2021)

## Abstract (arXiv)

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.