AI for DSP
DSP System Toolbox™ provides features to model a wavelet scattering network and detect anomalies using deep learning network in Simulink®.
The Wavelet Scattering block creates a framework for wavelet time scattering in the Simulink environment. Use this block to derive low-variance features from real-valued data, and then use those features in machine learning and deep learning applications. For more information, see Wavelet Scattering (Wavelet Toolbox). The Wavelet Scattering block requires Wavelet Toolbox™.
The Deep Signal
Anomaly Detector block detects real-time signal anomalies in
Simulink using a trained long short-term memory (LSTM) autoencoder deep
learning network model. You must first create and train a detector object in
MATLAB® using the deepSignalAnomalyDetector
function, and then configure the
block to use this model in Simulink. The Deep Signal Anomaly Detector block
requires Deep Learning Toolbox™.
Blocks
Wavelet Scattering | Model wavelet scattering network in Simulink (Since R2022b) |
Deep Signal Anomaly Detector | Detect signal anomalies using deep learning network in Simulink (Since R2024a) |
Topics
- Wavelet Scattering (Wavelet Toolbox)
Derive low-variance features from real-valued time series and image data.
- Fault Detection Using Wavelet Scattering and Recurrent Deep Networks (Wavelet Toolbox)
Classify faults in acoustic recordings of air compressors using a wavelet scattering network paired with a recurrent neural network. (Since R2021b)