Adaptive-gain algorithm on the fixed filters applied for active noise control headphone

Abstract

Active noise control (ANC) headphones are commonly used to reduce annoyed noise around users’ ears. Most commercial ANC headphones utilize specific filters with pre-trained coefficients due to their fast response and robustness. However, when dealing with primary noises coming from different directions, their noise reduction performance suffers significantly. Hence, we proposed an adaptive gain (AG) algorithm on the fixed filters with multi-reference method, in which the control signal is formed by adaptively weighting and summing the output signals of multiple fixed filters that have been pre-trained from primary noise with different directions of arrival. By combining the adaptive algorithm with the fixed filter approach, the proposed algorithm outperforms the conventional fixed ANC approach in noise reduction performance. This paper provides a theoretical analysis of the proposed algorithm’s step-size bound and time constant, demonstrating its robustness and fast convergence behavior. Furthermore, comparative simulations and real-time experiments with commercial ANC headphone and other adaptive algorithms show its efficacy in dealing with multiple noise sources in the actual scenario.