Wavelet Toolbox
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.
Getting Started
Learn the basics of Wavelet Toolbox
Time-Frequency Analysis
CWT, constant-Q transform, empirical mode decomposition, wavelet coherence, wavelet cross-spectrum
Discrete Wavelet Analysis
DWT, MODWT dual-tree小波变换,小波packets, multisignal analysis
Denoising and Compression
Wavelet shrinkage, nonparametric regression, block thresholding, multisignal thresholding
Machine Learning and Deep Learning
Wavelet scattering, wavelet-based techniques for machine learning and deep learning
Filter Banks
Orthogonal and biorthogonal wavelet and scaling filters, lifting
Code Generation
Generate C/C++ code and MEX functions for toolbox functions