用度量学习让降噪模型跨系统通用，免重训还能识新噪音。
Transferable Selective Virtual Sensing Active Noise Control Technique Based on Metric Learning
- 用度量学习让预训练的神经网络直接适配新降噪系统。
- 在突发宽带噪声和真实噪声下，降噪效果优于传统方法。
- 适合快速部署到新设备或未知噪声场景的主动降噪系统。
虚拟传感（VS）技术使主动降噪（ANC）系统能在远离物理误差麦克风的虚拟位置抑制噪声。合适的辅助滤波器（AF）可显著提升VS方法的效果。利用卷积神经网络（CNN）可自动选择适用于不同噪声类型的AF。然而，为不同ANC系统训练CNN模型通常耗时费力。为此，本文提出一种新方法——可迁移的自适应虚拟传感（Transferable Selective VS），将度量学习技术融入基于CNN的VS方法中。该方法使预训练的CNN可直接应用于新ANC系统而无需重新训练，并能处理未见过的噪声类型。数值仿真表明，所提方法在抑制突发变化的宽带噪声和真实噪声方面具有显著效果。
原文摘要 · Abstract (English)
Virtual sensing (VS) technology enables active noise control (ANC) systems to attenuate noise at virtual locations distant from the physical error microphones. Appropriate auxiliary filters (AF) can significantly enhance the effectiveness of VS approaches. The selection of appropriate AF for various types of noise can be automatically achieved using convolutional neural networks (CNNs). However, training the CNN model for different ANC systems is often labour-intensive and time-consuming. To tackle this problem, we propose a novel method, Transferable Selective VS, by integrating metric-learning technology into CNN-based VS approaches. The Transferable Selective VS method allows a pre-trained CNN to be applied directly to new ANC systems without requiring retraining, and it can handle unseen noise types. Numerical simulations demonstrate the effectiveness of the proposed method in attenuating sudden-varying broadband noises and real-world noises.
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