Derive features using time-frequency techniques for signal classification.
Signal Processing Layers
|Deep learning continuous wavelet transform (Since R2022b)
|Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (Since R2022a)
|Deep learning short-time Fourier transform (Since R2021a)
|Continuous wavelet transform filter bank
|Find abrupt changes in signal
|Find local maxima
|Maximal overlap discrete wavelet transform
|Rise time of positive-going bilevel waveform transitions
|Short-time Fourier transform (Since R2019a)
|Streamline signal frequency feature extraction (Since R2021b)
|Streamline signal time feature extraction (Since R2021a)
|Wavelet time scattering
Datastores and Data Management
|Create header structure for EDF or EDF+ file (Since R2021a)
|Get information about EDF/EDF+ file (Since R2020b)
|Read data from EDF/EDF+ file (Since R2020b)
|Create or modify EDF or EDF+ file (Since R2021a)
|Pad data by adding elements (Since R2023b)
|Resize data by adding or removing elements (Since R2023b)
|Trim data by removing elements (Since R2023b)
|Datastore for collection of signals (Since R2020a)
|Model wavelet scattering network in Simulink (Since R2022b)
- Classify Arm Motions Using EMG Signals and Deep Learning (Signal Processing Toolbox)
Classify arm motions using labeled EMG signals and a long short-term memory network.
- Detect Air Compressor Sounds in Simulink Using Wavelet Scattering (DSP System Toolbox)
Use the Wavelet Scattering block and a pretrained deep learning network to classify audio signals.
- Detect Anomalies in ECG Time-Series Data Using Wavelet Scattering and LSTM Autoencoder in Simulink (DSP System Toolbox)
Use wavelet scattering and deep learning network to detect anomalies in ECG signals.
- Manage Data Sets for Machine Learning and Deep Learning Workflows (Signal Processing Toolbox)
Organize, access, and manage data sets for different AI applications.