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Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data
[article]
2021
arXiv
pre-print
We propose FEDENHANCE, an unsupervised federated learning (FL) approach for speech enhancement and separation with non-IID distributed data across multiple clients. We simulate a real-world scenario where each client only has access to a few noisy recordings from a limited and disjoint number of speakers (hence non-IID). Each client trains their model in isolation using mixture invariant training while periodically providing updates to a central server. Our experiments show that our approach
arXiv:2105.04727v3
fatcat:o5p4ig6fljaotacm66c5gmjdb4