Classification

Classify signal attributes, perform signal segmentation via sequence-to-sequence classification

Derive features using time-frequency techniques for signal classification.

Functions

`cwtLayer`	Continuous wavelet transform (CWT) layer
`modwtLayer`	Maximal overlap discrete wavelet transform (MODWT) layer
`stftLayer`	Short-time Fourier transform layer

`dlcwt`	Deep learning continuous wavelet transform
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis
`dlstft`	Deep learning short-time Fourier transform
`cwtfilterbank`	Continuous wavelet transform filter bank
`findchangepts`	Find abrupt changes in signal
`findpeaks`	Find local maxima
`modwt`	Maximal overlap discrete wavelet transform
`risetime`	Rise time of positive-going bilevel waveform transitions
`stft`	Short-time Fourier transform
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction
`signalTimeFeatureExtractor`	Streamline signal time feature extraction
`waveletScattering`	Wavelet time scattering

`edfheader`	Create header structure for EDF or EDF+ file
`edfinfo`	Get information about EDF/EDF+ file
`edfread`	Read data from EDF/EDF+ file
`edfwrite`	Create or modify EDF or EDF+ file
`signalDatastore`	Datastore for collection of signals

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.

Classify electroencephaligraphic (EEG) time series from persons with and without epilepsy.

Construct wavelet-based feature embeddings to classify EEG signals from persons with and without epilepsy.

Classify heartbeat electrocardiogram data using deep learning and signal processing.

Classify heartbeat electrocardiogram data using deep learning and the continuous wavelet transform.

Classify spoken digits using both machine learning and deep learning techniques.

Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network.