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

Machine Learning and Deep Learning Classification Using Signal Feature Extraction Objects

Machine Learning and Deep Learning Classification Using Signal Feature Extraction Objects

Use signal feature extraction objects and AI-based classification to identify faulty bearing signals in mechanical systems.

Classify ECG Signals Using Long Short-Term Memory Networks

Classify ECG Signals Using Long Short-Term Memory Networks

Classify heartbeat electrocardiogram data using deep learning and signal processing.

Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences

Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences

Detect anomalies in acoustic data using wavelet scattering and the deepSignalAnomalyDetector object.

Denoise EEG Signals Using Differentiable Signal Processing Layers

Denoise EEG Signals Using Differentiable Signal Processing Layers

Remove EOG noise from EEG signals using deep learning regression.

Waveform Segmentation Using Deep Learning

Waveform Segmentation Using Deep Learning

Segment human electrocardiogram signals using time-frequency analysis and deep learning.

Export Labeled Data from Signal Labeler for Deep Learning Classification

Export Labeled Data from Signal Labeler for Deep Learning Classification

Label signals and export data using Signal Labeler to train a deep learning classifier.