Classify signal attributes, perform signal segmentation via sequence-to-sequence classification
Derive features using time-frequency techniques for signal classification.
Signal Processing Layers
|Continuous wavelet transform (CWT) layer|
|Maximal overlap discrete wavelet transform (MODWT) layer|
|Short-time Fourier transform layer|
|Deep learning continuous wavelet transform|
|Deep learning maximal overlap discrete wavelet transform and multiresolution analysis|
|Deep learning short-time Fourier transform|
|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|
|Streamline signal frequency feature extraction|
|Streamline signal time feature extraction|
|Wavelet time scattering|
Datastores and Data Import
|Create header structure for EDF or EDF+ file|
|Get information about EDF/EDF+ file|
|Read data from EDF/EDF+ file|
|Create or modify EDF or EDF+ file|
|Datastore for collection of signals|
|Wavelet Scattering||Model wavelet scattering network in Simulink|
- Manage Data Sets for Machine Learning and Deep Learning Workflows (Signal Processing Toolbox)
Organize, access, and manage data sets for different AI applications.
- 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.