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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 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.

What Is Wavelet Toolbox?

Machine Learning and Deep Learning with Wavelets

Derive low-variance features from real-valued time series and image data for use in machine learning and deep learning for classification and regression. Use continuous wavelet analysis to generate the 2D time-frequency maps of time series data, which can be used as inputs with deep convolutional neural networks (CNN).

Documentation | Examples

Time-Frequency Analysis

Analyze signals jointly in time and frequency and images jointly in space, spatial frequency, and angle with the continuous wavelet transform (CWT).Use wavelet coherence to reveal common time-varying patterns. Perform adaptive time-frequency analysis using nonstationary Gabor frames with the constant-Q transform (CQT).

Documentation | Examples

Signal Multiresolution Analyzer app depicting user interface.

Discrete Multiresolution Analysis

Perform decimated discrete wavelet transform (DWT) to analyze signals, images, and 3D Volumes in progressively finer octave bands. Implement nondecimated wavelet transforms. Decompose nonlinear or nonstationary processes into intrinsic modes of oscillation using techniques.

Documentation | Examples

Filter Banks

Use orthogonal wavelet filter banks like Daubechies, Coiflet, Haar and others to perform multiresolution analysis and feature detection. Design custom filter banks using the lifting method. Lifting also provides a computationally efficient approach for analyzing signal and images at different resolutions or scales.

Documentation | Examples

Denoising and Compression

Use wavelet and wavelet packet denoising techniques to retain features that are removed or smoothed by other denoising techniques. The Wavelet Signal Denoiser app lets you visualize and denoise 1D signals. Use wavelet and wavelet packets to compress signals and images by removing data without affecting perceptual quality.

Documentation | Examples

Accelerating wavelet coherence and other wavelet functions on GPUs.

Acceleration and Deployment

Speed up your code by using GPU and multicore processors for supported functions. Use MATLAB Coder™ to generate standalone ANSI-compliant C/C++ code from Wavelet Toolbox functions that have been enabled to support C/C++ code generation. Generate optimized CUDA code to run on NVIDIA^® GPUs for supported functions.

Documentation | Examples