EBEN: Extreme Bandwidth Extension Network Applied To Speech Signals Captured With Noise-Resilient Body-Conduction Microphones

Julien Hauret; Thomas Joubaud; Véronique Zimpfer; Éric Bavu

ICASSP 2023accepted0 citations

EBEN: Extreme Bandwidth Extension Network Applied To Speech Signals Captured With Noise-Resilient Body-Conduction Microphones

Julien Hauret, Thomas Joubaud, Véronique Zimpfer, Éric Bavu

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.

BibTeX

@inproceedings{icassp2023_ebenextremebandw,
  title = {EBEN: Extreme Bandwidth Extension Network Applied To Speech Signals Captured With Noise-Resilient Body-Conduction Microphones},
  author = {Julien Hauret and Thomas Joubaud and Véronique Zimpfer and Éric Bavu},
  booktitle = {ICASSP 2023},
  year = {2023}
}