Wavelet Toolbox™ provides functions and apps for analyzing and synthesizing signals and images. The toolbox includes algorithms for continuous wavelet analysis, wavelet coherence, synchrosqueezing, and data-adaptive time-frequency analysis. The toolbox also includes apps and functions for decimated and nondecimated discrete wavelet analysis of signals and images, including wavelet packets and dual-tree transforms.
Using continuous wavelet analysis, you can study the way spectral features evolve over time, identify common time-varying patterns in two signals, and perform time-localized filtering. Using discrete wavelet analysis, you can analyze signals and images at different resolutions to detect changepoints, discontinuities, and other events not readily visible in raw data. You can compare signal statistics on multiple scales, and perform fractal analysis of data to reveal hidden patterns.
With Wavelet Toolbox you can obtain a sparse representation of data, useful for denoising or compressing the data while preserving important features. Many toolbox functions support C/C++ code generation for desktop prototyping and embedded system 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 scattering, wavelet-based techniques for machine learning and deep learning
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)