Signal Processing Toolbox™ provides functionality to perform signal labeling, feature engineering, and dataset generation for machine learning and deep learning workflows.
Signal Analyzer | Visualize and compare multiple signals and spectra |
Signal Labeler | Label signal attributes, regions, and points of interest |
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 Audio Labeler.
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.
Music Genre Classification Using Wavelet Time Scattering (Wavelet Toolbox)
This example shows how to classify the genre of a musical excerpt using wavelet time scattering and the audio datastore.
Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)
This example shows how to classify human phonocardiogram (PCG) recordings using wavelet time scattering and a support vector machine (SVM) classifier.
Deep Learning in MATLAB (Deep Learning Toolbox)
Sequence Classification Using Deep Learning (Deep Learning Toolbox)