Visualize and denoise time series data
The Wavelet Signal Denoiser app is an interactive tool for visualizing and denoising real-valued 1-D signals and comparing results. With the app, you can:
Access all the signals in the MATLAB® workspace.
Easily adjust default parameters and apply different denoising techniques.
Visualize and compare results.
Export denoised signals to your workspace.
Recreate the denoised signal in your workspace by generating a MATLAB script.
The Wavelet Signal Denoiser app provides a way to work with multiple versions of denoised data simultaneously.
A typical workflow for denoising a signal and comparing results using the app is:
Start the app and load a 1-D signal from the MATLAB workspace. The app provides an initial denoised version of your data using default parameters.
Adjust the denoising parameters and produce multiple versions of the denoised signal.
Compare results and export the desired denoised signal to your workspace.
To apply the same denoising parameters to other signals in your workspace, generate a MATLAB script and modify it as you see fit.
MATLAB Toolstrip: On the Apps tab, under Signal Processing and Communications, click Wavelet Signal Denoiser .
MATLAB command prompt: Enter
This example shows how to denoise a 1-D signal using the app default settings.
Load the noisy Doppler signal.
Start the Wavelet Signal Denoiser app by choosing it from the Apps tab on the MATLAB® Toolstrip. You can also start the app by typing
waveletSignalDenoiser at the MATLAB command prompt.
Load the noisy Doppler signal from the workspace into the app by clicking Load Signal in the toolstrip. From the list of workspace variables that can be loaded into the app, select
noisdopp and click OK.
The app displays the original signal,
noisdopp, the denoised signal,
noisdopp1, and the coarse scale approximation,
To toggle what plots are visible, you can:
Click Signals ▼ in the toolstrip and use the drop-down menu to toggle the visibility of the original and approximation plots.
Click individual signals in the plot legend.
Wavelet— Wavelet family
Wavelet family used to denoise the signal, specified as one of the following:
sym — Symlets
bior — Biorthogonal spline wavelets
coif — Coiflets
db — Daubechies wavelets
fk — Fejér-Korovkin wavelets
Method— Denoising method
Denoising method to apply, specified as one of the following:
Bayes — Empirical Bayes
BlockJS — Block James-Stein
FDR — False Discovery Rate
Minimax — Minimax Estimation
SURE — Stein's Unbiased Risk Estimate
UniversalThreshold — Universal Threshold
Rule— Thresholding rule
Thresholding rule to use. The possible rules you can specify for different denoising methods are as follows:
Block James-Stein —
Empirical Bayes —
False Discovery Rate —
Minimax Estimation —
Stein's Unbiased Risk Estimate —
Universal Threshold —
To denoise more than one signal simultaneously, you can run multiple instances of the Wavelet Signal Denoiser app.