论文标题
EBEN：极限带宽扩展网络应用于用噪声触发的人体传导麦克风捕获的语音信号
EBEN: Extreme bandwidth extension network applied to speech signals captured with noise-resilient body-conduction microphones
论文作者
论文摘要
在本文中，我们介绍了极端带宽扩展网络（EBEN），这是一种生成对抗网络（GAN），可增强使用人体传导麦克风测量的音频。这种类型的捕获设备以语音带宽为代价抑制环境噪声，从而需要信号增强技术以恢复宽带语音信号。 EBEN利用原始捕获的语音的多曲线分解，以降低数据时间域的维度，并更好地控制全带信号。该多型表示形式被馈送到类似U-NET的模型，该模型采用了功能和对抗性损失的组合来恢复增强的音频信号。我们还从提出的歧视架构中的这种原始表示中受益。我们的方法可以通过轻巧的生成器和实时兼容操作来实现最先进的结果。
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.
