Feedback Controller Optimization for Active Noise Control Headphones Considering Frequency Response Mismatch between Microphone and Human Ear

Abstract

This paper presents an investigation on the feedback controller design for active noise control headphones under the condition that the frequency responses of the primary and secondary paths corresponding to the feedback microphone do not match to the ones corresponding to the human ear. The influence of such mismatches on the performance are analyzed first, and then an optimization method is proposed to enhance the comprehensive performance at the human ear. In the proposed method, the feedback loop is constructed directly with the feedback microphone and any extra filters of the virtual sensing techniques are avoided. Cascade biquad filters are used as the controller, which is in accordance with current applications. A differential evolution algorithm was used to solve the proposed optimization problem, and the optimal parameters of the controller were found. It has been shown by the experimental results that, at the dummy head ear position, good noise reduction performance could be obtained at the low frequency band with limited noise enhancement for high frequencies, even if large frequency response mismatches exist.

Uncertainty Constraint on Headphone Secondary Path Function for Designing Cascade Biquad Feedback Controller with Improved Noise Reduction Performance

“…Analog circuits have significant errors, and the FIR controllers will introduce additional delay and cannot meet the requirements of current ANC headphones. Wang et al [27] and An et al [28] implemented controller designs using the IIR filters and optimized them with the genetic optimization algorithm (GA) combined with the Nelder-Mead (NM) algorithm and differential evolution (DE) algorithm, respectively. In the former controller, the application of dual optimization increased the system complexity, while the latter controller based on the DE algorithm operates directly in continuous space, facilitating the global minimum search but bringing high computational demand [29].…”