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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. You can also compute the maximal overlap discrete wavelet transform (MODWT) and MODWT multiresolution analysis (MRA) within a deep learning network.

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, and extract features


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cwtLayerContinuous wavelet transform (CWT) layer (Since R2022b)
modwtLayerMaximal overlap discrete wavelet transform (MODWT) layer (Since R2022b)
stftLayerShort-time Fourier transform layer (Since R2021b)
array2cwtfiltersConvert deep-learning CWT filter tensor to filter bank matrix (Since R2022b)
cwtfilterbankContinuous wavelet transform filter bank
cwtfilters2arrayConvert CWT filter bank to reduced-weight tensor for deep learning (Since R2022b)
dlcwtDeep learning continuous wavelet transform (Since R2022b)
dlmodwtDeep learning maximal overlap discrete wavelet transform and multiresolution analysis (Since R2022a)
dlstftDeep learning short-time Fourier transform (Since R2021a)
lwt1-D lifting wavelet transform (Since R2021a)
melSpectrogramMel spectrogram
modwptMaximal overlap discrete wavelet packet transform
modwtMaximal overlap discrete wavelet transform
waveletScatteringWavelet time scattering
wentropyWavelet entropy
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 (Since R2020a)
labeledSignalSetCreate labeled signal set
signalLabelDefinitionCreate signal label definition