Machine Learning and Deep Learning with Wavelets
Derive low-variance features from real-valued time series and image data for classification and regression with machine learning and deep learning models. Use continuous wavelet analysis to generate 2D time-frequency maps of time series data, which can be used as inputs to deep convolutional neural networks (CNN).
Analyze signals jointly in time and frequency and images jointly in space, spatial frequency, and angle with the continuous wavelet transform (CWT). Use the Time-Frequency Analyzer app to visualize scalograms of real- and complex-valued signals. Perform adaptive time-frequency analysis using nonstationary Gabor frames with the constant-Q transform (CQT).
Discrete Multiresolution Analysis
Use the 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 empirical mode decomposition (EMD).
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 packet algorithms to compress signals and images by removing data without affecting perceptual quality.
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.