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Signal Processing Using Deep Learning

Extend deep learning workflows with signal processing applications

Apply deep learning to signal processing by using Deep Learning Toolbox™ together with Signal Processing Toolbox™, Wavelet Toolbox™, Radar Toolbox, or DSP System Toolbox™. For audio and speech processing applications, see Audio Processing Using Deep Learning. For applications in wireless communications, see Wireless Communications Using Deep Learning.


Signal LabelerLabel signal attributes, regions, and points of interest, and extract features


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cwtLayerContinuous wavelet transform (CWT) layer
modwtLayerMaximal overlap discrete wavelet transform (MODWT) layer
stftLayerShort-time Fourier transform layer
dlcwtDeep learning continuous wavelet transform
dlmodwtDeep learning maximal overlap discrete wavelet transform and multiresolution analysis
dlstftDeep learning short-time Fourier transform
cwtContinuous 1-D wavelet transform
modwtMaximal overlap discrete wavelet transform
stftShort-time Fourier transform
audioFeatureExtractorStreamline audio feature extraction
signalFrequencyFeatureExtractorStreamline signal frequency feature extraction
signalTimeFeatureExtractorStreamline signal time feature extraction
waveletScatteringWavelet time scattering
edfheaderCreate header structure for EDF or EDF+ file
edfinfoGet information about EDF/EDF+ file
edfreadRead data from EDF/EDF+ file
edfwriteCreate or modify EDF or EDF+ file
audioDatastoreDatastore for collection of audio files
signalDatastoreDatastore for collection of signals
labeledSignalSetCreate labeled signal set
signalLabelDefinitionCreate signal label definition
signalMaskModify and convert signal masks and extract signal regions of interest
countlabelsCount number of unique labels
filenames2labelsGet list of labels from filenames
folders2labelsGet list of labels from folder names
splitlabelsFind indices to split labels according to specified proportions


Wavelet ScatteringModel wavelet scattering network in Simulink