Next-Generation ANC: Integrating Dynamic Fixed-Filter Strategies With Extended Kalman Filtering for Enhanced Noise Suppression

Abstract

The hybrid selective fixed-filter active noise control with filtered reference normalized least mean square (SFANC-FxNLMS) method struggles in dynamic noise environments due to its reliance on static filters, which limits effectiveness when noise characteristics change rapidly. The generative fixed-filter active noise control with Kalman filtering (GFANC-Kalman) approach offers improved adaptability by dynamically adjusting the filtering process but may still underperform in complex noise scenarios. The dynamic fixed-filter active noise control with extended Kalman filter (DFANC-EKF) method overcomes these limitations by integrating an extended Kalman filter with a 2D convolutional neural network for advanced feature extraction. This integration enables the system to better capture and adapt to intricate noise patterns, significantly enhancing noise reduction. Numerical simulations using real-world noise data validate the DFANC-EKF approach's superior performance across various challenging scenarios.