Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data

• URL: http://arxiv.org/abs/2105.04727v1
• Date: Tue, 11 May 2021 00:47:18 GMT
• Title: Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data
• Authors: Efthymios Tzinis, Jonah Casebeer, Zhepei Wang, Paris Smaragdis
• Abstract summary: We propose FEDENHANCE, an unsupervised federated learning (FL) approach for speech enhancement and separation. 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. Our experiments show that our approach achieves competitive enhancement performance compared to IID training on a single device.
• Score: 27.51123821674191
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 achieves competitive enhancement performance compared to IID training on a single device and that we can further facilitate the convergence speed and the overall performance using transfer learning on the server-side. Moreover, we show that we can effectively combine updates from clients trained locally with supervised and unsupervised losses. We also release a new dataset LibriFSD50K and its creation recipe in order to facilitate FL research for source separation problems.

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