FPGA-based Adaptive Noise Cancellation System for Audio Systems

This paper presents the Field Programmable Gate Array(FPGA) implementation of an active noise cancellation(ANC) system to enhance audio quality in noisy environments. Three adaptive filtering algorithms—least mean square(LMS), normalized least mean square(NLMS), and recursive least mean square(RLS) are analyzed for noise cancellation effectiveness and computational cost. While Least Mean Square is computationally efficient, it has the highest error rate. Normalized Least Mean Square adapts faster, improving noise suppression, whereas Recursive Least Mean Square offers the best accuracy at the cost of higher computational complexity. Implemented on a Zynq-7000 Field Programmable Gate Array (FPGA) using MATLAB Simulink and Hardware Description Language translation, simulations show Recursive Least Mean Square outperforms Normalized Least Mean Square with lower error, while Least Mean Square performs the worst. This study highlights the trade-offs between noise reduction and computational efficiency in FPGA-based Active Noise Cancellation systems.