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Working with Signals

Multiresolution analysis, wavelet time scattering, continuous wavelet transform, nondecimated discrete wavelet transform, Wigner-Ville distribution, mel spectrogram

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 LabelerLabel signal attributes, regions, and points of interest


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cwtfilterbankContinuous wavelet transform filter bank
dlstftDeep learning short-time Fourier transform
lwt1-D lifting wavelet transform
melSpectrogramMel spectrogram
modwptMaximal overlap discrete wavelet packet transform
modwtMaximal overlap discrete wavelet transform
stftLayerShort-time Fourier transform layer
waveletScatteringWavelet time scattering
wvdWigner-Ville distribution and smoothed pseudo Wigner-Ville distribution
audioDatastoreDatastore for collection of audio files
augmentedImageDatastoreTransform batches to augment image data
imageDatastoreDatastore for image data
signalDatastoreDatastore for collection of signals
labeledSignalSetCreate labeled signal set
signalLabelDefinitionCreate signal label definition


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.

Featured Examples