Audio active noise reduction algorithm based on sigmoid function and normalization processing

In this paper, a novel active noise reduction algorithm SFxNLMS (sigmoid filtered-x normalized least mean square) is proposed, which aims to solve the problem of slow convergence of the traditional FxLMS (filtered-x least mean square) algorithm in the field of active noise reduction. Based on the FxLMS algorithm, the algorithm introduces a sigmoid function to nonlinearly process the filtered error signal, and adopts a normalization strategy to optimize the learning step size, thus improving the convergence speed and stability of the algorithm. Using Gaussian white noise as the input signal, the FxLMS, SFxLMS and SFxNLMS algorithms are used for the noise active noise reduction simulation comparison experiments to compare and analyze the performance of these three algorithms. The results show that compared with the other two algorithms, the average noise reduction and convergence speed after active noise reduction using the SFxNLMS algorithm are substantially improved.