Classificazione

Classificare gli attributi del segnale, eseguire la segmentazione del segnale tramite la classificazione sequenza-sequenza

Derivare le feature utilizzando tecniche tempo-frequenza per la classificazione dei segnali.

Funzioni

`cwtLayer`	Continuous wavelet transform layer (Da R2022b)
`icwtLayer`	Inverse continuous wavelet transform layer (Da R2024b)
`modwtLayer`	Maximal overlap discrete wavelet transform layer (Da R2022b)
`stftLayer`	Short-time Fourier transform layer (Da R2021b)
`istftLayer`	Inverse short-time Fourier transform layer (Da R2024a)

`dlcwt`	Deep learning continuous wavelet transform (Da R2022b)
`dlicwt`	Deep learning inverse continuous 1-D wavelet transform (Da R2024b)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (Da R2022a)
`dlstft`	Deep learning short-time Fourier transform (Da R2021a)
`dlistft`	Deep learning inverse short-time Fourier transform (Da R2024a)
`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 (Da R2021b)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (Da R2021a)
`signalTimeFrequencyFeatureExtractor`	Streamline signal time-frequency feature extraction (Da R2024a)
`timeFrequencyScattering`	Joint time-frequency scattering (Da R2024b)
`waveletScattering`	Wavelet time scattering

`edfheader`	Create header structure for EDF or EDF+ file (Da R2021a)
`edfinfo`	Get information about EDF/EDF+ file
`edfread`	Read data from EDF/EDF+ file
`edfwrite`	Create or modify EDF or EDF+ file (Da R2021a)
`paddata`	Pad data by adding elements (Da R2023b)
`resize`	Resize data by adding or removing elements (Da R2023b)
`trimdata`	Trim data by removing elements (Da R2023b)
`signalDatastore`	Datastore for collection of signals

Model wavelet scattering network in Simulink (Da 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. (Da R2022a)
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 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.

Classify musical instruments using joint time-frequency features paired with a 3-D convolutional network.

Use signal feature extraction objects and AI-based classification to identify faulty bearing signals in mechanical systems.

Use the locally interpretable model-agnostic explanation technique to interpret decision-making processes of a deep learning network.

Label signals and export data using Signal Labeler to train a deep learning classifier.

Classify electroencephalographic (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.