Title: Wavelet analysis-based techniques for processing voice and video signals in network communications

Authors: Fangshu Liu

Addresses: College of Artificial Intelligence and Big Data, Zibo Vocational Institute, Zibo 255300, China

Abstract: Aiming at the shortcomings of traditional wavelet analysis techniques in signal processing, the study proposes an improved threshold wavelet analysis method to optimise its signal denoising effect. Firstly, the important components of wavelet analysis theory are discussed. Secondly, based on the soft threshold and hard threshold functions for signal denoising, an adjustment factor is introduced to optimise the convergence for communication signals smaller than the threshold. The outcomes demonstrate that the improved wavelet thresholding approach efficiently lowers noise while preserving the precise properties of the original signal. The technique generates a reconstructed signal with lesser distortion and fewer noise residuals when compared to previous approaches. The signal-to-noise ratio of the improved thresholded wavelet output is consistently over 90, and the distortion deviation is very near to 2. In conclusion, this method of communication signal processing can enhance communication quality while reducing interference from the outside world.

Keywords: network communication; signal processing; wavelet analysis; modulation factor; threshold function.

DOI: 10.1504/IJCSYSE.2024.142770

International Journal of Computational Systems Engineering, 2024 Vol.8 No.3/4, pp.293 - 302

Received: 20 Mar 2023
Accepted: 19 Jul 2023
Published online: 21 Nov 2024 *

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