EBEN: Extreme bandwidth extension network applied to speech signals captured with noise-resilient body-conduction microphones

Julien Hauret; Thomas Joubaud; Véronique Zimpfer; Éric Bavu

doi:10.1109/icassp49357.2023.10096301

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arXiv · 2210.14090

EBEN: Extreme bandwidth extension network applied to speech signals captured with noise-resilient body-conduction microphones

Julien Hauret·Thomas Joubaud·Véronique Zimpfer·Éric Bavu

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Abstract

In this paper, we present Extreme Bandwidth Extension Network (EBEN), a Generative Adversarial network (GAN) that enhances audio measured with body-conduction microphones. This type of capture equipment suppresses ambient noise at the expense of speech bandwidth, thereby requiring signal enhancement techniques to recover the wideband speech signal. EBEN leverages a multiband decomposition of the raw captured speech to decrease the data time-domain dimensions, and give better control over the full-band signal. This multiband representation is fed to a U-Net-like model, which adopts a combination of feature and adversarial losses to recover an enhanced audio signal. We also benefit from this original representation in the proposed discriminator architecture. Our approach can achieve state-of-the-art results with a lightweight generator and real-time compatible operation.

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Julien Hauret, Thomas Joubaud, Véronique Zimpfer, Éric Bavu. 2022-10-25. EBEN: Extreme bandwidth extension network applied to speech signals captured with noise-resilient body-conduction microphones. https://doi.org/10.1109/icassp49357.2023.10096301

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