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.
|Create labeled signal set
|Create signal label definition
|Count number of unique labels (Since R2021a)
|Get list of labels from filenames (Since R2022b)
|Get list of labels from folder names (Since R2021a)
|Find indices to split labels according to specified proportions (Since R2021a)
|Modify and convert signal masks and extract signal regions of interest (Since R2020b)
|Convert binary mask to matrix of ROI limits (Since R2020b)
|Extend signal regions of interest to left and right (Since R2020b)
|Extract signal regions of interest (Since R2020b)
|Merge signal regions of interest (Since R2020b)
|Remove signal regions of interest (Since R2020b)
|Shorten signal regions of interest from left and right (Since R2020b)
|Convert matrix of ROI limits to binary mask (Since R2020b)
|Label signal samples with values within a specified range (Since R2023a)
Datastores and Data Management
|Get information about EDF/EDF+ file (Since R2020b)
|Create or modify EDF or EDF+ file (Since R2021a)
|Create header structure for EDF or EDF+ file (Since R2021a)
|Read data from EDF/EDF+ file (Since R2020b)
|Datastore for collection of signals (Since R2020a)
|Resize data by adding or removing elements (Since R2023b)
|Pad data by adding elements (Since R2023b)
|Trim data by removing elements (Since R2023b)
|Deep learning short-time Fourier transform (Since R2021a)
|Short-time Fourier transform layer (Since R2021b)
|Find abrupt changes in signal
|Find local maxima
|Find signal location using similarity search
|Fourier synchrosqueezed transform
|Estimate instantaneous bandwidth (Since R2021a)
|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 (Since R2021b)
|Streamline signal time feature extraction (Since R2021a)
|Zero-crossing rate (Since R2021b)
- 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.
- Anomaly Detection Using Autoencoder and Wavelets
Use wavelet-extracted features and an autoencoder to detect arc signals in a DC system.
- 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.