Time series denoising based on wavelet decomposition and cross-correlation between the residuals and the denoised signal

Barbara Cannas, Andrea Murari, Fabio Pisano

Research output: Contribution to conferencePaper

Abstract

In this paper, a new denoising method, based on the wavelet transform of the noisy signal, is described. The method implements a variable thresholding, whose optimal value is determined by analyzing the cross-correlation between the denoised signal and the residuals and by applying different criteria depending on the particular decomposition level. The residuals are defined as the difference between the noisy signal and the denoised signal. The procedure is suitable for denoising signals in real situations when the noiseless signal is not known. The results, obtained with synthetic data generated by well-known chaotic systems, show the very competitive performance of the proposed technique.
Original languageEnglish
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventIASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2013 - , Austria
Duration: 1 Jan 2013 → …

Conference

ConferenceIASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2013
CountryAustria
Period1/1/13 → …

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Cannas, B., Murari, A., & Pisano, F. (2013). Time series denoising based on wavelet decomposition and cross-correlation between the residuals and the denoised signal. Paper presented at IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2013, Austria. https://doi.org/10.2316/P.2013.798-097