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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.

apps

signal analyzer	visualize and compare multiple signals and spectra
	label signal attributes, regions, and points of interest, and extract features
	view edf or edf files

functions

signal labeling

	create labeled signal set
	create signal label definition
	count number of unique labels
	get list of labels from filenames
	get list of labels from folder names
	find indices to split labels according to specified proportions
	modify and convert signal masks and extract signal regions of interest
	convert binary mask to matrix of roi limits
	extend signal regions of interest to left and right
	extract signal regions of interest
	merge signal regions of interest
	remove signal regions of interest
	shorten signal regions of interest from left and right
	convert matrix of roi limits to binary mask
	label signal samples with values within a specified range

datastores and data import

	get information about edf/edf file
	create or modify edf or edf file
	create header structure for edf or edf file
	read data from edf/edf file
	datastore for collection of signals

feature extraction

	deep learning short-time fourier transform
	short-time fourier transform layer
	find abrupt changes in signal
	find local maxima
	find signal location using similarity search
	fourier synchrosqueezed transform
	estimate instantaneous bandwidth
	estimate instantaneous frequency
	spectral entropy of signal
	periodogram power spectral density estimate
	spectral kurtosis from signal or spectrogram
	power bandwidth
	analyze signals in the frequency and time-frequency domains
	welch’s power spectral density estimate
	streamline signal frequency feature extraction
	streamline signal time feature extraction
	zero-crossing rate

anomaly detection

create signal anomaly detector

topics

organize, access, and manage data sets for different ai applications.
decide which app to use to label ground truth data: image labeler, video labeler, ground truth labeler, lidar labeler, signal labeler, or medical image labeler.
(phased array system toolbox)
classify radar and communications waveforms using the wigner-ville distribution (wvd) and a deep convolutional neural network (cnn).
label radar signals with signal labeler (radar toolbox)
label the time and frequency features of pulse radar signals with added noise.
(radar toolbox)
classify pedestrians and bicyclists based on their micro-doppler characteristics using deep learning and time-frequency analysis.
(wavelet toolbox)
classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.
train a spoken digit recognition network on out-of-memory auditory spectrograms using a transformed datastore.
denoise speech signals using fully connected and convolutional neural networks.
classify ecg signals using the continuous wavelet transform and a deep convolutional neural network.

related information

deep learning in matlab (deep learning toolbox)
(deep learning toolbox)