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.
Time-frequency analysis using continuous wavelet transform, wavelet coherence, constant-Q transform, empirical mode decomposition, and Hilbert-Huang transform
Wavelet Signal Denoiser app for denoising time-series data
Decimated wavelet packet and wavelet transforms, including wavelet leaders for fractal analysis
Nondecimated techniques, including dual-tree, stationary wavelet, maximal overlap discrete wavelet, and wavelet packet transforms
Signal, image denoising, and compression, including matching pursuit
Lifting method for constructing custom wavelets