Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments

• URL: http://arxiv.org/abs/2106.03671v1
• Date: Mon, 7 Jun 2021 14:51:39 GMT
• Title: Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments
• Authors: Alexandru Nelus, Rene Glitza, and Rainer Martin
• Abstract summary: We introduce a realistic and challenging, multi-source and multi-room acoustic environment. We present an improved clustering control strategy that takes into account the variability of the acoustic scene. The proposed approach is optimized using clustering-based measures and validated via a network-wide classification task.
• Score: 75.8001929811943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we introduce a realistic and challenging, multi-source and multi-room acoustic environment and an improved algorithm for the estimation of source-dominated microphone clusters in acoustic sensor networks. Our proposed clustering method is based on a single microphone per node and on unsupervised clustered federated learning which employs a light-weight autoencoder model. We present an improved clustering control strategy that takes into account the variability of the acoustic scene and allows the estimation of a dynamic range of clusters using reduced amounts of training data. The proposed approach is optimized using clustering-based measures and validated via a network-wide classification task.

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