Data-Driven Uncertainty Modeling for Robust Feedback Active Noise Control in Headphones

Active noise control (ANC) has become popular for reducing noise and thus enhancing user comfort in headphones. Although feedback control offers an effective way to implement ANC, it is restricted by uncertainty of the controlled system (e.g., that arises from differing wearing situations). Widely used unstructured models that capture these variations tend to overestimate the uncertainty and thus restrict ANC performance. As a remedy, this work explores uncertainty models whose shapes are derived from experimentally determined measurement data to improve ANC performance for over-ear and in-ear headphones. The controller optimization based on these models is described, and an ANC prototype is implemented to compare the performances associated with conventional and proposed modeling approaches. Extensive measurements with human wearers confirm the robustness and indicate a performance improvement over conventional methods. The results allow to increase the active attenuation of ANC headphones by several decibels at no loss of stability.