# Documentation

### This is machine translation

Translated by
Mouseover text to see original. Click the button below to return to the English version of the page.

To view all translated materials including this page, select Japan from the country navigator on the bottom of this page.

# Wavelet Signal Denoiser

Visualize and denoise time series data

## Description

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:

1. 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.

2. Adjust the denoising parameters and produce multiple versions of the denoised signal.

3. Compare results and export the desired denoised signal to your workspace.

4. To apply the same denoising parameters to other signals in your workspace, generate a MATLAB script and modify it as you see fit.

## Open the Wavelet Signal Denoiser App

• MATLAB Toolstrip: On the Apps tab, under Signal Processing and Communications, click Wavelet Signal Denoiser .

• MATLAB command prompt: Enter `waveletSignalDenoiser`.

## Parameters

expand all

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

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 $\sqrt{2\mathrm{ln}\left(·\right)}$

Thresholding rule to use. The possible rules you can specify for different denoising methods are as follows:

• Block James-Stein — `James-Stein`

• Empirical Bayes — `Median`, `Mean`, `Soft`, `Hard`

• False Discovery Rate — `Hard`

• Minimax Estimation — `Soft`, `Hard`

• Stein's Unbiased Risk Estimate — `Soft`, `Hard`

• Universal Threshold —`Soft`, `Hard`

## Tips

• To simultaneously denoise more than one signal, you can run multiple instances of the Wavelet Signal Denoiser.