Self-Adaptive noise distribution network for Speech Enhancement with heterogeneous data of Cross-Silo Federated learning

  • We propose a SASE model with adaptive noise distribution, which achieves state of the art results on the VioceBank+DEMAND dataset.
  • We simulated the federated learning setting of a real environment and verified the robustness of the proposed SASE noise reduction model in a real environment through experiments and visualization.
  • The proposed SASE model is computed based on the complex domain, and the TF-GA block is used to extract richer information of speech distribution and noise distribution, while SA-GOEA and SA-GUEA are adaptive to learn the distribution mask of noise.
  • In this paper, we propose a model aggregation optimization weighting strategy that is more applicable to FLbased speech enhancement tasks.

Dependencies

  • python >=3.6 (3.8.5 was used in the experiments)
  • PyTorch == 1.10.0+cu113
  • flwr == 2.0.1

Github:

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Lingaraj Senapati
Hey There! I am Lingaraj Senapati, the Co-founder of lingarajtechhub.com My skills are Freelance, Web Developer & Designer, Corporate Trainer, Digital Marketer & Youtuber.
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