AI for Signals
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 Analyzer||Visualize and compare multiple signals and spectra|
|Signal Labeler||Label signal attributes, regions, and points of interest, and extract features|
|EDF File Analyzer||View EDF or EDF+ files|
|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|
|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|
|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|
- Manage Data Sets for Machine Learning and Deep Learning Workflows
Organize, access, and manage data sets for different AI applications.
- 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 Medical Image Labeler.
- Radar and Communications Waveform Classification Using Deep Learning (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.
- Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox)
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis.
- Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine 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.
- Denoise Speech Using Deep Learning Networks
Denoise speech signals using fully connected and convolutional neural networks.
- Classify Time Series Using Wavelet Analysis and Deep Learning
Classify ECG signals using the continuous wavelet transform and a deep convolutional neural network.
- Deep Learning in MATLAB (Deep Learning Toolbox)
- Sequence Classification Using Deep Learning (Deep Learning Toolbox)