AI for Signals
Signal Processing Toolbox™ provides functionality to perform signal labeling, feature engineering, classification, and data set 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
- AI with MATLAB and Python
Develop AI for signal processing in MATLAB® using Python® coexecution
- 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
Featured Examples
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 heartbeat electrocardiogram data using deep learning and signal processing.
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
Remove EOG noise from EEG signals using deep learning regression.
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
Label signals and export data using Signal Labeler to train a deep learning classifier.
