Robust Soft-Constrained Spatially Selective Active Noise Control for Hearables Under Secondary Path Variations

TL;DR

This paper introduces a robust optimization method for spatially selective active noise control in hearables, effectively handling secondary path variations to improve stability and performance consistency.

Contribution

It proposes a soft-constrained optimization framework that accounts for secondary path uncertainties, enhancing robustness in noise control systems.

Findings

Slight reduction in mean performance compared to ideal conditions

Significant reduction in performance variability under path mismatch

Validated through simulations and real-time experiments

Abstract

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 and experiments on a real-time control platform show that the proposed approach slightly reduces mean performance relative to the matched case but substantially narrows the performance spread under secondary path mismatch. The proposed framework therefore provides a practical design strategy when accurate secondary path estimates are unavailable.