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

Functions

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labeledSignalSetCreate labeled signal set
signalLabelDefinitionCreate signal label definition
signalDatastoreDatastore for collection of signals
findchangeptsFind abrupt changes in signal
findpeaksFind local maxima
findsignalFind signal location using similarity search
fsstFourier synchrosqueezed transform
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

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

This example shows how to classify radar waveform types of generated synthetic data using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

Pedestrian and Bicyclist Classification Using Deep Learning (Phased Array System Toolbox)

This example shows how to classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.

Signal Classification Using Wavelet-Based Features and Support Vector Machines

Classify human electrocardiogram signals using wavelet-based feature extraction and a support vector machine classifier.

Classify Time Series Using Wavelet Analysis and Deep Learning

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

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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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 Network

Label QRS Complexes and R Peaks of ECG Signals Using Deep 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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Wavelet Time Scattering for ECG Signal Classification

Wavelet Time Scattering for ECG Signal Classification

Classify human electrocardiogram signals using wavelet time scattering and an SVM classifier.

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Music Genre Classification Using Wavelet Time Scattering

Music Genre Classification Using Wavelet Time Scattering

Classify the genre of a musical excerpt using wavelet time scattering and the audio datastore.

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Wavelet Time Scattering Classification of Phonocardiogram Data

Wavelet Time Scattering Classification of Phonocardiogram Data

Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.

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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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Signal Processing Toolbox Documentation
Support
5G Development with MATLAB

5G Development with MATLAB

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