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Filtering of Audio Signals Using Discrete Wavelet Transforms

Author

Listed:
  • H. K. Nigam

    (Department of Mathematics, Central University of South Bihar, Gaya 824236, Bihar, India)

  • H. M. Srivastava

    (Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8W 3R4, Canada
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
    Department of Mathematics and Informatics, Azerbaijan University, 71 Jeyhun Hajibeyli Street, Baku AZ1007, Azerbaijan
    Section of Mathematics, International Telematic University Uninettuno, I-00186 Rome, Italy)

Abstract

Nonlinear diffusion has been proved to be an indispensable approach for the removal of noise in image processing. In this paper, we employ nonlinear diffusion for the purpose of denoising audio signals in order to have this approach also recognized as a powerful tool for audio signal processing. We apply nonlinear diffusion to wavelet coefficients obtained from different filters associated with orthogonal and biorthogonal wavelets. We use wavelet decomposition to keep signal components well-localized in time. We compare denoising results using nonlinear diffusion with wavelet shrinkage for different wavelet filters. Our experiments and results show that the denoising is much improved by using the nonlinear diffusion process.

Suggested Citation

  • H. K. Nigam & H. M. Srivastava, 2023. "Filtering of Audio Signals Using Discrete Wavelet Transforms," Mathematics, MDPI, vol. 11(19), pages 1-12, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4117-:d:1250356
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    Cited by:

    1. Mario Vozza & Joseph Polden & Giulio Mattera & Gianfranco Piscopo & Silvestro Vespoli & Luigi Nele, 2024. "Explaining the Anomaly Detection in Additive Manufacturing via Boosting Models and Frequency Analysis," Mathematics, MDPI, vol. 12(21), pages 1-17, October.

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