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HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement
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Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models. In this paper, we show that this success can be extended to other tasks of conditional audio generation. In particular, building upon HiFi vocoders, we propose a novel HiFi++ general framework for bandwidth extension and speech enhancement. We show that with the improved generator architecture, HiFi++ performs better or comparably with the state-of-the-art in these tasks while spending significantly less computational resources. The effectiveness of our approach is validated through a series of extensive experiments.
Forward citations
Cited by 2 Pith papers
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Query-Based Asymmetric Modeling with Decoupled Input-Output Rates for Speech Restoration
TF-Restormer restores degraded speech at arbitrary input-output sampling rates in a single model, using a heavy encoder and a lightweight query-based decoder to generate missing high-frequency bands.
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Voice-ENHANCE: Speech Restoration using a Diffusion-based Voice Conversion Framework
A diffusion voice conversion model, conditioned on clean speaker embeddings and HuBERT content features, is applied after a generative speech restorer to achieve state-of-the-art-comparable speech quality.
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