La traduction de cette page n'est pas à jour. Cliquez ici pour voir la dernière version en anglais.
Prétraitement et extraction de caractéristiques
Extrayez les caractéristiques pertinentes du signal avec des fonctions en ligne de commande ou l’application Signal Feature Extractor pour les utiliser dans des modèles de Machine Learning.
Applications
| EDF File Analyzer | View EDF or EDF+ files (depuis R2021a) |
| Signal Analyzer | Visualiser et comparer plusieurs signaux et spectres |
| Signal Labeler | Label signal attributes, regions, and points of interest |
| Signal Feature Extractor | Extract and analyze signal features (depuis R2025a) |
Fonctions
Informations connexes
- Spectral Descriptors (Audio Toolbox)
Rubriques
- Use Signal Feature Extractor App
Extract and rank features from signals interactively. Prepare signal datasets for classification tasks.
- Manage Data Sets for Machine Learning and Deep Learning Workflows
Organize, access, and manage data sets for different AI applications.
- Accelerate Signal Feature Extraction and Classification Using a Parallel Pool of Workers
Use parallel computing to extract signal multidomain features for bearing fault detection. (depuis R2024b)
- Accelerate Signal Feature Extraction and Classification Using a GPU
Use a graphical processing unit (GPU) to extract signal multidomain features for bearing fault detection. (depuis R2024b)
- View, Preprocess, and Write EDF File
View and preprocess data stored in an EDF file.
Sélection d՚exemples
Machine Learning and Deep Learning Classification Using Signal Feature Extraction Objects
Use signal feature extraction objects and AI-based classification to identify faulty bearing signals in mechanical systems.
Feature Selection Based on Deep Learning Interpretability for Signal Classification Applications
Use the locally interpretable model-agnostic explanation technique to interpret decision-making processes of a deep learning network.
Human Activity Recognition Using Signal Feature Extraction and Machine Learning
Extract features from smartphone sensor signals and use them to classify human activity.
Time-Frequency Feature Embedding with Deep Metric Learning
Construct wavelet-based feature embeddings to classify EEG signals from persons with and without epilepsy.
Signal Classification Using Wavelet-Based Features and Support Vector Machines
Classify electrocardiogram signals using features derived from wavelets and an autoregressive model.
