针对耳戴设备次路径变化，提出鲁棒软约束优化方法提升降噪稳定性。
Robust Soft-Constrained Spatially Selective Active Noise Control for Hearables Under Secondary Path Variations
- 基于多人测量的次路径估计集，最小化平均代价求解单一控制滤波器
- 实测显示性能波动显著缩小，平均性能仅略低于理想匹配情况
- 适合对佩戴差异敏感的真机部署场景，尤其适用于个性化降噪系统
空间定向主动降噪（SSANC）耳戴设备旨在抑制特定方向噪声至耳膜，同时保留选定方向语音。现有系统通常依赖精确的次路径估计（从扬声器到内部误差麦克风），但实际中该路径随用户个体差异和佩戴方式变化，导致性能下降并影响系统稳定性。本文提出一种鲁棒软约束优化框架，通过在一组基于人体测量的次路径估计上最小化平均代价，计算单一控制滤波器。仿真表明，该方法在次路径失配下性能均值略低于理想匹配，但性能波动大幅缩小。实时实验使用头身模拟器验证，结果与仿真高度一致。
原文摘要 · Abstract (English)
Spatially selective active noise control (SSANC) hearables aim to attenuate noise from certain directions at the eardrum while preserving desired speech arriving from selected directions. Existing SSANC systems typically assume an accurate estimate of the secondary path from the loudspeaker to the inner error microphone. In practice, however, this path varies across users and device fits, which can degrade performance and compromise system stability. This paper proposes a robust soft-constrained optimization framework that computes a single control filter by minimizing the average cost over a set of secondary-path estimates derived from human measurements. Simulations show that the proposed approach achieves slightly lower mean performance than the matched case but substantially narrows the performance spread under secondary-path mismatch. Real-time experiments using the tested head-and-torso simulator show good agreement with the simulations.
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