Wavelet Toolbox

Analyze and synthesize signals and images using wavelets

Wavelet Toolbox™ provides apps and functions for analyzing and synthesizing signals and images. You can detect events like anomalies, change points, and transients, and denoise and compress data. Wavelet and other multiscale techniques can be used to analyze data at different time and frequency resolutions and to decompose signals and images into their various components. You can use wavelet techniques to reduce dimensionality and extract discriminating features from signals and images to train machine and deep learning models.

With Wavelet Toolbox you can interactively denoise signals, perform multiresolution and wavelet analysis, and generate MATLAB^® code. The toolbox includes algorithms for continuous and discrete wavelet analysis, wavelet packet analysis, multiresolution analysis, wavelet scattering, and other multiscale analysis.

Many toolbox functions support C/C++ and CUDA^® code generation for desktop prototyping and embedded system 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