EBEN: Extreme bandwidth extension network applied to speech signals captured with noise-resilient body-conduction microphones
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866916326611615744 |
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| author | Hauret, Julien Joubaud, Thomas Zimpfer, Véronique Bavu, Éric |
| author_facet | Hauret, Julien Joubaud, Thomas Zimpfer, Véronique Bavu, Éric |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2210_14090 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | EBEN: Extreme bandwidth extension network applied to speech signals captured with noise-resilient body-conduction microphones Hauret, Julien Joubaud, Thomas Zimpfer, Véronique Bavu, Éric Audio and Speech Processing Sound 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. |
| title | EBEN: Extreme bandwidth extension network applied to speech signals captured with noise-resilient body-conduction microphones |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2210.14090 |