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 extractions, 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.
Learn the basics of Wavelet Toolbox
CWT, constant-Q transform, empirical mode decomposition, wavelet coherence, wavelet cross-spectrum
DWT, MODWT, dual-tree wavelet transform, shearlets, wavelet packets, multisignal analysis
Wavelet shrinkage, nonparametric regression, block thresholding, multisignal thresholding
Wavelet-based techniques for machine learning and deep learning, GPU acceleration, hardware deployment, signal labeling
Orthogonal and biorthogonal wavelet and scaling filters, lifting
Generate C/C++ and CUDA code and MEX functions, and run functions on a graphics processing unit (GPU)