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AI for Signals

Signal labeling, feature engineering, dataset generation, anomaly detection

Signal Processing Toolbox™ provides functionality to perform signal labeling, feature engineering, and dataset generation for machine learning and deep learning workflows. The toolbox also offers an autoencoder object that you can train and use to detect anomalies in signal data.


Signal AnalyzerVisualize and compare multiple signals and spectra
Signal LabelerLabel signal attributes, regions, and points of interest, and extract features
EDF File AnalyzerView EDF or EDF+ files (Da R2021a)
Experiment Manager Design and run experiments to train and compare deep learning networks (Da R2020a)


espandi tutto

labeledSignalSetCreate labeled signal set
signalLabelDefinitionCreate signal label definition
countlabelsCount number of unique labels (Da R2021a)
filenames2labelsGet list of labels from filenames (Da R2022b)
folders2labelsGet list of labels from folder names (Da R2021a)
framelblPartition label sequence into frames (Da R2024a)
framesigPartition signal into frames (Da R2024a)
splitlabelsFind indices to split labels according to specified proportions (Da R2021a)
signalMaskModify and convert signal masks and extract signal regions of interest (Da R2020b)
binmask2sigroiConvert binary mask to matrix of ROI limits (Da R2020b)
extendsigroiExtend signal regions of interest to left and right (Da R2020b)
extractsigroiExtract signal regions of interest (Da R2020b)
mergesigroiMerge signal regions of interest (Da R2020b)
removesigroiRemove signal regions of interest (Da R2020b)
shortensigroiShorten signal regions of interest from left and right (Da R2020b)
sigroi2binmaskConvert matrix of ROI limits to binary mask (Da R2020b)
sigrangebinmaskLabel signal samples with values within a specified range (Da R2023a)
edfinfoGet information about EDF/EDF+ file (Da R2020b)
edfwriteCreate or modify EDF or EDF+ file (Da R2021a)
edfheaderCreate header structure for EDF or EDF+ file (Da R2021a)
edfreadRead data from EDF/EDF+ file (Da R2020b)
signalDatastoreDatastore for collection of signals (Da R2020a)
resizeResize data by adding or removing elements (Da R2023b)
paddataPad data by adding elements (Da R2023b)
trimdataTrim data by removing elements (Da R2023b)
findchangeptsFind abrupt changes in signal
findpeaksFind local maxima
fsstFourier synchrosqueezed transform
stftShort-time Fourier transform
spectrogramSpectrogram using short-time Fourier transform
tfridgeTime-frequency ridges
instbwEstimate instantaneous bandwidth (Da R2021a)
instfreqEstimate instantaneous frequency
powerbwPower bandwidth
pspectrumAnalyze signals in the frequency and time-frequency domains
spectralCrestSpectral crest for signals and spectrograms
spectralEntropySpectral entropy for signals and spectrograms
spectralFlatnessSpectral flatness for signals and spectrograms
spectralKurtosisSpectral kurtosis for signals and spectrograms
spectralSkewnessSpectral skewness for signals and spectrograms
scalarFeatureOptionsStore information for converting feature vectors to scalar values (Da R2024a)
signalFrequencyFeatureExtractorStreamline signal frequency feature extraction (Da R2021b)
signalTimeFeatureExtractorStreamline signal time feature extraction (Da R2021a)
signalTimeFrequencyFeatureExtractorStreamline signal time-frequency feature extraction (Da R2024a)
timeFrequencyScalarFeatureOptionsStore information for converting time-frequency feature vectors to scalar values (Da R2024a)
zerocrossrateZero-crossing rate (Da R2021b)
dlstftDeep learning short-time Fourier transform (Da R2021a)
dlistftDeep learning inverse short-time Fourier transform (Da R2024a)
stftLayerShort-time Fourier transform layer (Da R2021b)
istftLayerInverse short-time Fourier transform layer (Da R2024a)
deepSignalAnomalyDetectorCreate signal anomaly detector (Da R2023a)