AmbiDrop: 基于Ambisonics的阵列无关神经语音增强
AmbiDrop: Ambisonics-Based Array-Agnostic Neural Speech Enhancement
AI总结：
提出AmbiDrop框架，通过将任意麦克风阵列信号转换为Ambisonics域并采用通道丢弃训练，实现与阵列几何无关的鲁棒语音增强，在未见阵列和传感器故障下表现优异。
摘要：
多通道深度神经网络（DNN）显著提升了语音增强性能，但通常受限于对固定麦克风阵列几何结构的依赖，导致在未见或非规则配置上泛化能力差。当前的阵列无关方法通常依赖高复杂度架构或海量多样化数据集，但仍难以泛化到分布外的布局。本文深入分析了AmbiDrop——一个近期提出的框架，通过利用理想Ambisonics作为DNN输入实现几何无关性。在训练期间采用通道丢弃层模拟Ambisonics编码误差，使学习过程与物理传感器排列解耦。推理时，来自任意阵列配置的麦克风信号通过Ambisonics信号匹配（ASM）转换到Ambisonics域再进行处理。大量实验表明，AmbiDrop在多种未见模拟阵列和真实录音中保持高鲁棒性。此外，我们的结果显示该框架对传感器故障具有弹性，即使在网络规模缩减时仍保持有效，使其非常适合部署在资源受限的边缘设备和多功能可穿戴硬件上。
英文摘要：
Multichannel Deep Neural Networks (DNNs) have significantly improved speech enhancement performance; however, they typically remain constrained by reliance on fixed microphone array geometries, leading to poor generalization on unseen or irregular configurations. Current array-agnostic approaches often rely on high-complexity architectures or massive, diverse datasets, yet they still struggle to generalize to out-of-distribution layouts. In this paper, we present an in-depth analysis of AmbiDrop, a recently proposed framework that achieves geometry independence by leveraging ideal Ambisonics as the DNN input. By employing a channel-wise dropout layer during training to simulate Ambisonics encoding errors, AmbiDrop decouples the learning process from the physical sensor arrangement. During inference, microphone signals from arbitrary array configurations are transformed into the Ambisonics domain via Ambisonics Signal Matching (ASM) before processing. Extensive experiments demonstrate that AmbiDrop maintains high robustness across a diverse suite of unseen simulated arrays and real-world recordings. Furthermore, our results show that the framework is resilient to sensor failures and remains effective even with reduced network scales, making it highly suitable for deployment on resource-constrained edge devices and versatile wearable hardware.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
