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Wavelet Toolbox

Perform time-frequency and wavelet analysis of signals and images

Wavelet Toolbox™ provides apps and functions for the time-frequency analysis of signals and multiscale analysis of images. You can denoise and compress data, and detect anomalies, change-points, and transients. The toolbox enables data-centric artificial intelligence (AI) workflows by providing time-frequency transforms and automated feature extraction, including scattering transforms, continuous wavelet transforms (scalograms), Wigner-Ville distribution, and empirical mode decomposition. You can extract edges and oriented features from images using wavelet, wavelet packet, and shearlet transforms.

The apps let you interactively perform time-frequency analysis, signal denoising, or image analysis, and generate MATLAB® scripts to reproduce or automate your work.

You can generate C/C++ and CUDA® code from toolbox functions for embedded deployment.

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Learn the basics of Wavelet Toolbox

Time-Frequency Analysis

CWT, constant-Q transform, empirical mode decomposition, wavelet coherence, wavelet cross-spectrum

Discrete Multiresolution Analysis

DWT, MODWT, dual-tree wavelet transform, shearlets, wavelet packets, multisignal analysis

Denoising and Compression

Wavelet shrinkage, nonparametric regression, block thresholding, multisignal thresholding

AI for Signals and Images

Wavelet-based techniques for machine learning and deep learning, GPU acceleration, hardware deployment, signal labeling

Filter Banks

Orthogonal and biorthogonal wavelet and scaling filters, lifting

Code Generation and GPU Support

Generate C/C++ and CUDA code and MEX functions, and run functions on a graphics processing unit (GPU)