This paper presents a configurable version of Extreme Bandwidth Extension Network (EBEN), a Generative Adversarial Network (GAN) designed to improve audio captured with body-conduction microphones. We show that although these microphones significantly reduce environmental noise, this insensitivity to ambient noise happens at the expense of the bandwidth of the speech signal acquired by the wearer of the devices. The obtained captured signals therefore require the use of signal enhancement techniques to recover the full-bandwidth speech. EBEN leverages a configurable multiband decomposition of the raw captured signal. This decomposition allows the data time domain dimensions to be reduced and the full band signal to be better controlled. The multiband representation of the captured signal is processed through a U-Net-like model, which combines feature and adversarial losses to generate an enhanced speech signal. We also benefit from this original representation in the proposed configurable discriminators architecture. The configurable EBEN approach can achieve state-of-the-art enhancement results on synthetic data with a lightweight generator that allows real-time processing.

翻译：本文提出一种可配置版本的极端带宽扩展网络（EBEN），这是一种旨在改善体导麦克风捕获音频质量的生成对抗网络（GAN）。研究表明，尽管这类麦克风能显著降低环境噪声，但环境不敏感性是以牺牲佩戴者所获取语音信号的带宽为代价的。因此，所捕获的信号需要采用信号增强技术来恢复全带宽语音。EBEN利用对原始捕获信号的可配置多频带分解，该分解既能降低数据时域维度，又能更好地控制全频带信号。捕获信号的多频带表示通过类似U-Net的模型进行处理，该模型结合特征损失与对抗损失生成增强后的语音信号。同时，从这一独特表示中获益，本文提出了可配置判别器架构。可配置EBEN方法能够使用轻量级生成器在合成数据上取得最先进的增强效果，并支持实时处理。