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In this paper, we present Extreme Bandwidth Extension Network (EBEN), aGenerative Adversarial network (GAN) that enhances audio measured withbody-conduction microphones. This type of capture equipment suppresses ambientnoise at the expense of speech bandwidth, thereby requiring signal enhancementtechniques to recover the wideband speech signal. EBEN leverages a multibanddecomposition of the raw captured speech to decrease the data time-domaindimensions, and give better control over the full-band signal. This multibandrepresentation is fed to a U-Net-like model, which adopts a combination offeature and adversarial losses to recover an enhanced audio signal. We alsobenefit from this original representation in the proposed discriminatorarchitecture. Our approach can achieve state-of-the-art results with alightweight generator and real-time compatible operation.

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