Main Content


Signal source separation, denoising, signal recovery

Use deep learning techniques to denoise signals. Use differentiable time-frequency transforms to reconstruct signals when there is missing information.


expand all

cwtLayerContinuous wavelet transform (CWT) layer (Since R2022b)
modwtLayerMaximal overlap discrete wavelet transform (MODWT) layer (Since R2022b)
stftLayerShort-time Fourier transform layer (Since R2021b)
cwtmag2sigSignal reconstruction from CWT magnitude (Since R2023b)
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)
cwtfilterbankContinuous wavelet transform filter bank
findchangeptsFind abrupt changes in signal
findpeaksFind local maxima
modwtMaximal overlap discrete wavelet transform
risetime Rise time of positive-going bilevel waveform transitions
stftShort-time Fourier transform (Since R2019a)
stftmag2sigSignal reconstruction from STFT magnitude (Since R2020b)
signalFrequencyFeatureExtractorStreamline signal frequency feature extraction (Since R2021b)
signalTimeFeatureExtractorStreamline signal time feature extraction (Since R2021a)
waveletScatteringWavelet time scattering
edfheaderCreate header structure for EDF or EDF+ file (Since R2021a)
edfinfoGet information about EDF/EDF+ file (Since R2020b)
edfreadRead data from EDF/EDF+ file (Since R2020b)
edfwriteCreate or modify EDF or EDF+ file (Since R2021a)
paddataPad data by adding elements (Since R2023b)
resizeResize data by adding or removing elements (Since R2023b)
trimdataTrim data by removing elements (Since R2023b)
signalDatastoreDatastore for collection of signals (Since R2020a)


Wavelet ScatteringModel wavelet scattering network in Simulink (Since R2022b)