This tool applies wavelet-thresholding–based noise reduction to the input signal using a Julia implementation of discrete wavelet decomposition.

It automatically separates noise from meaningful signal components and reconstructs a cleaner version of the signal, preserving important transients and features while reducing broadband noise.


Various options can be set directly from the context menu:




Wavelet Type


Selects the wavelet family and filter shape used for decomposition.


  • Haar – Fast, simple, blocky transitions; good for step-like signals.
  • Daubechies4 / Daubechies8 – Compactly supported wavelets with increasing smoothness; suitable for general denoising.
  • Symlet6 – Symmetric, smooth wavelet; often produces very natural-looking denoised signals.
  • Coiflet4 – Balanced wavelet with good time–frequency localization.
  • Battle4 – Smooth, spline-like wavelet; good for strongly oscillatory signals.
  • Beylkin – Very smooth wavelet for detailed feature preservation.



Levels (3–6)


  • Defines the number of wavelet decomposition levels.
  • Higher levels remove noise on larger time scales but may also smooth out fine details.
  • Lower levels focus on removing only high-frequency noise.


Threshold Method


Controls how the denoising threshold is computed.


  • Universal – Classic Donoho universal threshold; strong noise suppression, risk of over-smoothing.
  • Robust – Uses robust estimation of noise level; suitable for signals with non-Gaussian noise.
  • Fixed3Sigma – Applies a fixed threshold of 3× estimated noise sigma; useful for predictable noise levels.


Shrinkage Mode


Determines how coefficients above or below the threshold are modified.


  • Soft – Reduces coefficients smoothly toward zero; produces smoother, more natural results.
  • Hard – Sets coefficients below threshold to zero while keeping others unchanged; preserves sharp features but may introduce artifacts.