AI for DSP
DSP System Toolbox™ provides features to extract signal statistics and detect signal anomalies using deep learning network in Simulink®.
The Time Feature Extractor block extracts time-domain features from the signal such as the mean, RMS, standard deviation, SNR, and SINAD.
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
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)
Featured Examples
Detect Air Compressor Sounds in Simulink Using Wavelet Scattering
Use the Wavelet Scattering block and a pretrained deep learning network to classify audio signals.
Detect Air Compressor Sounds on a Raspberry Pi Using Wavelet Scattering and TensorFlow Lite Network
Generate and deploy Raspberry Pi® code to detect air compressor sounds using the Wavelet Scattering Simulink block and a pretrained deep learning network.
Fault Detection and Localization in Three-Phase Power Transmission Using Deep Signal Anomaly Detector in Simulink
Detect faults in three-phase power transmission using the Deep Signal Anomaly Detector block.
Detect Anomalies in ECG Data Using Wavelet Scattering and LSTM Autoencoder in Simulink
Use wavelet scattering and deep learning network to detect anomalies in ECG signals.
Real-Time Noise Detection on Raspberry Pi Using Deep Signal Anomaly Detector
Detect the presence of noise on a Raspberry Pi device.
Deploy Air Compressor Fault Detection Using Wavelet Scattering and TensorFlow Lite on Desktops
Generate and deploy code to detect air compressor sounds using Wavelet Scattering and a TensorFlow Lite deep learning network.
