Main Content

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

Featured Examples