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.

翻译：本文提出极端带宽扩展网络（EBEN），这是一种生成对抗网络（GAN），用于增强骨传导麦克风捕获的音频。此类捕获设备以牺牲语音带宽为代价抑制环境噪声，因而需要信号增强技术来恢复宽带语音信号。EBEN利用原始捕获语音的多频带分解来降低数据时域维度，并提升对全频带信号的控制能力。该多频带表示被输入至类似U-Net的模型，该模型结合特征损失与对抗损失来恢复增强后的音频信号。我们还在所提出的判别器架构中受益于这种原始表示。我们的方法能够以轻量级生成器实现实时兼容操作，并达到业界最优性能。