Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter

Abstract

The most significant issues for a thousand users is the unwanted or loud signals in sound files, it is impossible to eliminate or minimize these noise signals without knowledge their types and ranges. To overcome this issue, present an Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter (EIS-SNR-VBRAF) is proposed. Initially, the data is collected from Adaptive Myriad Filter using Time-Varying Noise and Signal-Dependent Parameters dataset. Then, the data is given for Pre-processing section, Variational Bayesian based Robust Adaptive Filter (VBRAF) is used to reduce the signal noise from Gaussian Noise, Traffic noise and Audio signal back ground noise. The proposed technique is implemented and efficacy of EIS-SNR-VBRAF technique is assessed by support of numerous performances such as BER, Signal-to-Noise-Ratio (SNR), Mean Squared Error, Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error, Structural Similarity Index, Cross-Correlation and Computational Complexity is analyzed. The proposed EIS-SNR-VBRAF method attains 21.18%, 23.52% and 23.65% lower RMSE, 21.52%, 21.76% and 23.24% higher PSNR, 21.19%, 21.73% and 20.48% higher Structural Similarity Index are compared with existing methods like early detection of mechanical malfunctions in vehicles utilizing sound signal processing (ED-MMV-SSP),noise reduction in infrasound signals based on mask coefficient binary weighting generalized cross correlation non-negative matrix factorization algorithm (NRID-MCWG-NMFA) and Click-event sound detection in automotive industry using machine/deep learning (CE-SDSI-DL) respectively.