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arXiv · 2203.13086

HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement

Pavel Andreev·Aibek Alanov·Oleg Ivanov·Dmitry Vetrov
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Abstract

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.

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Pavel Andreev, Aibek Alanov, Oleg Ivanov, Dmitry Vetrov. 2022-03-24. HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement. https://doi.org/10.1109/icassp49357.2023.10097255

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