Classification

Classer les attributs des signaux et segmenter les signaux via la classification séquence à séquence

Déterminez des caractéristiques avec des techniques temps-fréquence pour la classification de signaux.

Fonctions

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

`dlcwt`	Deep learning continuous wavelet transform (depuis R2022b)
`dlicwt`	Deep learning inverse continuous 1-D wavelet transform (depuis R2024b)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (depuis R2022a)
`dlstft`	Deep learning short-time Fourier transform (depuis R2021a)
`dlistft`	Deep learning inverse short-time Fourier transform (depuis R2024a)
`cwtfilterbank`	Continuous wavelet transform filter bank
`findchangepts`	Find abrupt changes in signal
`findpeaks`	Trouver les maxima locaux
`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 (depuis R2021b)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (depuis R2021a)
`signalTimeFrequencyFeatureExtractor`	Streamline signal time-frequency feature extraction (depuis R2024a)
`timeFrequencyScattering`	Joint time-frequency scattering (depuis R2024b)
`waveletScattering`	Wavelet time scattering

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

Model wavelet scattering network in Simulink (depuis 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. (depuis 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.