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Machine Learning and Deep Learning for Signals

Signal labeling, feature engineering, dataset generation

Signal Processing Toolbox™ provides functionality to perform signal labeling, feature engineering, and dataset generation for machine learning and deep learning workflows.

Apps

Signal AnalyzerVisualize and compare multiple signals and spectra
Signal LabelerLabel signal attributes, regions, and points of interest
EDF File AnalyzerView EDF or EDF+ files

Functions

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labeledSignalSetCreate labeled signal set
signalLabelDefinitionCreate signal label definition
countlabelsCount number of unique labels
folders2labelsGet list of labels from folder names
splitlabelsFind indices to split labels according to specified proportions
signalMaskModify and convert signal masks and extract signal regions of interest
binmask2sigroiConvert binary mask to matrix of ROI limits
extendsigroiExtend signal regions of interest to left and right
extractsigroiExtract signal regions of interest
mergesigroiMerge signal regions of interest
removesigroiRemove signal regions of interest
shortensigroiShorten signal regions of interest from left and right
sigroi2binmaskConvert matrix of ROI limits to binary mask
edfinfoGet information about EDF/EDF+ file
edfwriteCreate or modify EDF or EDF+ file
edfheaderCreate header structure for EDF or EDF+ file
edfreadRead data from EDF/EDF+ file
signalDatastoreDatastore for collection of signals
dlstftDeep learning short-time Fourier transform
findchangeptsFind abrupt changes in signal
findpeaksFind local maxima
findsignalFind signal location using similarity search
fsstFourier synchrosqueezed transform
instbwEstimate instantaneous bandwidth
instfreqEstimate instantaneous frequency
pentropySpectral entropy of signal
periodogramPeriodogram power spectral density estimate
pkurtosisSpectral kurtosis from signal or spectrogram
powerbwPower bandwidth
pspectrumAnalyze signals in the frequency and time-frequency domains
pwelchWelch’s power spectral density estimate

Topics

Choose an App to Label Ground Truth Data

Decide which app to use to label ground truth data: Image Labeler, Video Labeler, Ground Truth Labeler, Lidar Labeler, Signal Labeler, or Audio Labeler.

Radar and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)

This example shows how to classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox)

Classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.

Music Genre Classification Using Wavelet Time Scattering (Wavelet Toolbox)

This example shows how to classify the genre of a musical excerpt using wavelet time scattering and the audio datastore.

Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)

This example shows how to classify human phonocardiogram (PCG) recordings using wavelet time scattering and a support vector machine (SVM) classifier.

Train Spoken Digit Recognition Network Using Out-of-Memory Features

Train a spoken digit recognition network on out-of-memory auditory spectrograms using a transformed datastore.

Related Information

Deep Learning in MATLAB (Deep Learning Toolbox)

Sequence Classification Using Deep Learning (Deep Learning Toolbox)

Featured Examples

Classify ECG Signals Using Long Short-Term Memory Networks

Classify ECG Signals Using Long Short-Term Memory Networks

Classify heartbeat electrocardiogram data using deep learning and signal processing.

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Waveform Segmentation Using Deep Learning

Waveform Segmentation Using Deep Learning

Segment human electrocardiogram signals using time-frequency analysis and deep learning.

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Iterative Approach for Creating Labeled Signal Sets with Reduced Human Effort

Iterative Approach for Creating Labeled Signal Sets with Reduced Human Effort

Use deep learning to decrease the human effort required to label signals.

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Generate Synthetic Signals Using Conditional Generative Adversarial Network

Generate Synthetic Signals Using Conditional Generative Adversarial Network

Use a conditional generative adversarial network to produce synthetic signals.

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Spoken Digit Recognition with Custom Log Spectrogram Layer and Deep Learning

Spoken Digit Recognition with Custom Log Spectrogram Layer and Deep Learning

Classify spoken digits using a deep convolutional neural network and a custom spectrogram layer.

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Label Signal Attributes, Regions of Interest, and Points

Label Signal Attributes, Regions of Interest, and Points

Use Signal Labeler to label attributes, regions, and points of interest in a set of whale songs.

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Label QRS Complexes and R Peaks of ECG Signals Using Deep Learning Network

Label QRS Complexes and R Peaks of ECG Signals Using Deep Learning Network

Use Signal Labeler to locate and label QRS complexes and R peaks of ECG signals.

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Label Spoken Words in Audio Signals Using External API

Label Spoken Words in Audio Signals Using External API

Use Signal Labeler to label spoken words in an audio signal.

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Labeling Radar Signals with Signal Labeler

Labeling Radar Signals with Signal Labeler

Label the time and frequency features of pulse radar signals with added noise.

Denoise Speech Using Deep Learning Networks

Denoise Speech Using Deep Learning Networks

Denoise speech signals using fully connected and convolutional neural networks.

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Classify Time Series Using Wavelet Analysis and Deep Learning

Classify Time Series Using Wavelet Analysis and Deep Learning

Classify ECG signals using the continuous wavelet transform and a deep convolutional neural network.

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Deploy Signal Segmentation Deep Network on Raspberry Pi

Deploy Signal Segmentation Deep Network on Raspberry Pi

Generate a MEX function and a standalone executable to perform waveform segmentation on a Raspberry Pi™.

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Signal Processing Toolbox Documentation

Support

Deep Learning for Signal Processing with MATLAB

Deep Learning for Signal Processing with MATLAB

Download white paper