Working with Signals
Wavelet scattering enables you to produce low-variance data representations that minimize differences within a class while preserving discriminability across classes. Wavelet scattering requires few user-specified parameters to produce compact representations of data which are robust against time shifts on a scale you define. You can use these representations in conjunction with machine learning algorithms for classification and regression.
You can use the continuous wavelet transform (CWT) to generate 2-D time-frequency maps of time series data, which can be used with 2-D convolutional networks. Generating time-frequency representations for use in deep CNNs is a powerful approach for signal classification. The ability of the CWT to simultaneously capture steady-state and transient behavior in time series data makes the wavelet-based time-frequency representation particularly robust when paired with deep CNNs.
With a Signal Processing Toolbox™ license you can include the short-time Fourier transform
into your machine learning and deep learning workflows. You can also use
Signal Labeler (Signal Processing Toolbox) to label signals for analysis or for use in
machine learning and deep learning applications. Signal
Labeler saves data as
labeledSignalSet objects. With a Audio Toolbox™ license you can Import and Play Audio File Data in Signal Labeler (Signal Processing Toolbox).
You can also use
melSpectrogram (Audio Toolbox) for feature extraction.
|Signal Labeler||Label signal attributes, regions, and points of interest, and extract features|
|Convert deep-learning CWT filter tensor to filter bank matrix|
|Continuous wavelet transform filter bank|
|Convert CWT filter bank to reduced-weight tensor for deep learning|
|Deep learning continuous wavelet transform|
|Deep learning maximal overlap discrete wavelet transform and multiresolution analysis|
|Deep learning short-time Fourier transform|
|1-D lifting wavelet transform|
|Maximal overlap discrete wavelet packet transform|
|Maximal overlap discrete wavelet transform|
|Wavelet time scattering|
|Wigner-Ville distribution and smoothed pseudo Wigner-Ville distribution|
- Wavelet Scattering
Derive low-variance features from real-valued time series and image data.
- Wavelet Scattering Invariance Scale and Oversampling
Learn how changing the invariance scale and oversampling factor affects the output of the wavelet scattering transform.