AI for Signals
Signal labeling, feature engineering, classification, dataset generation, anomaly
detection
Signal Processing Toolbox™ provides functionality to perform signal labeling, feature engineering, classification 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.
Categories
- Classification
Classify signal attributes, perform signal segmentation using sequence-to-sequence classification
- Regression
Signal denoising, phase recovery, and source separation
- Preprocessing and Feature Extraction
Extract signal features in time, frequency, and time-frequency domains
- Signal Labeling
Manual and automated labeling of signal attributes, regions of interest, and points
- Anomaly Detection
Detect signal anomalies using AI models, including deep learning networks
- AI Applications
Audio, biomedical, predictive maintenance, radar and wireless
- Embedded AI Systems
Deploy deep learning into embedded targets and GPUs
Related Information
- Deep Learning in MATLAB (Deep Learning Toolbox)
- How to Set Up and Manage Experiments in MATLAB