Active Noise Control System Based on Hybrid Fixed Filter Method and FxLMS Algorithm

This project looks at the built‑in problems of most real‑time active noise control (ANC) systems. Those systems usually use the filtered‑x least mean square (FxLMS) algorithm to adjust the filter coefficients. Even though FxLMS is common, it often converges slowly, takes a long time for the noise reduction to settle, and can become unstable when sudden acoustic changes occur. To solve these issues, we explore a hybrid control method that combines pre‑trained fixed filter coefficients with the adaptive FxLMS algorithm. The system works by: * Analyzing the frequency content of the incoming noise. * Picking the pre‑trained filter coefficients that best match that spectrum. * Using those coefficients as a

well‑chosen starting point for the FxLMS algorithm, which then fine‑tunes the control parameters in real time. Starting the adaptive process near the optimal setting speeds up convergence dramatically and makes the noise‑cancelling system more resistant to brief disturbances. The project also gives detailed instructions for modeling, simulating, and testing this hybrid setup. It provides a clear framework for measuring how well the system reduces noise and how stable it stays across different acoustic conditions.

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