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Machine learning y deep learning para señales
Signal Processing Toolbox™ proporciona funcionalidades para realizar el etiquetado de señales, la ingeniería de características y la generación de conjuntos de datos en los flujos de trabajo de machine learning y deep learning.
Apps
Signal Analyzer | Visualizar y comparar múltiples señales y espectros |
Signal Labeler | Etiquete atributos de señal, regiones y puntos de interés |
EDF File Analyzer | View EDF or EDF+ files |
Funciones
Temas
- Choose an App to Label Ground Truth Data
Decide which app to use to label ground truth data: Image Labeler, Video Labeler, Ground Truth Labeler, Lidar Labeler, or Signal Labeler.
- Radar and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)
This example shows how to classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).
- Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox)
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.
- Music Genre Classification Using Wavelet Time Scattering (Wavelet Toolbox)
Classify the genre of a musical excerpt using wavelet time scattering and the audio datastore.
- Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.
- Train Spoken Digit Recognition Network Using Out-of-Memory Features
Train a spoken digit recognition network on out-of-memory auditory spectrograms using a transformed datastore.
Información relacionada
- Deep Learning in MATLAB (Deep Learning Toolbox)
- Sequence Classification Using Deep Learning (Deep Learning Toolbox)