An Improved LMS Adaptive Filtering Speech Enhancement Algorithm

Abstract

In order to improve the accuracy of speech recognition, the input speech signal is usually denoised first, which is typically done using the Least Mean Square (LMS) algorithm. To address the drawback that the fixed-step LMS algorithm in adaptive filtering cannot achieve a balance between convergence speed and steady-state error, this paper proposes a variable-step LMS algorithm based on an improved inverse hyperbolic sine function. In this paper, the improved algorithm is applied to speech enhancement, and the performance of this algorithm is compared with several other improved algorithms. The simulation results show that the improved algorithm takes better care of the conflict between convergence speed and steady-state error, and the algorithm has an obvious denoising effect for noisy speech, which effectively improves the clarity and intelligibility of speech and provides prerequisites for speech recognition.