Signal Processing Toolbox™ provides functionality to perform signal labeling, feature engineering, and dataset generation for machine learning and deep learning workflows.
|Create labeled signal set|
|Create signal label definition|
|Count number of unique labels|
|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|
|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|
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)
Classify the genre of a musical excerpt using wavelet time scattering and the audio datastore.
Wavelet Time Scattering Classification of Phonocardiogram Data (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.