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 |
EDF File Analyzer | View EDF or EDF+ files |
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 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)
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.
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.
Deep Learning in MATLAB (Deep Learning Toolbox)
Sequence Classification Using Deep Learning (Deep Learning Toolbox)