Preprocesamiento y extracción de características
Extraiga características significativas de señales utilizando funciones de línea de comandos o la app Feature Extractor para utilizarlas en modelos de machine learning.
Apps
| EDF File Analyzer | Visualizar archivos EDF o EDF+ (Desde R2021a) |
| Signal Analyzer | Visualizar y comparar múltiples señales y espectros |
| Signal Labeler | Etiquetar atributos de señal, regiones y puntos de interés |
| Signal Feature Extractor | Extract and analyze signal features (Desde R2025a) |
Funciones
Información relacionada
- Spectral Descriptors (Audio Toolbox)
Temas
- View, Preprocess, and Write EDF File (Signal Processing Toolbox)
View and preprocess data stored in an EDF file.
- Manage Data Sets for Machine Learning and Deep Learning Workflows (Signal Processing Toolbox)
Organize, access, and manage data sets for different AI applications.
- Accelerate Signal Feature Extraction and Classification Using a Parallel Pool of Workers (Signal Processing Toolbox)
Use parallel computing to extract signal multidomain features for bearing fault detection. (Desde R2024b)
- Accelerate Signal Feature Extraction and Classification Using a GPU (Signal Processing Toolbox)
Use a graphical processing unit (GPU) to extract signal multidomain features for bearing fault detection. (Desde R2024b)
Ejemplos destacados
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.
