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Régression
R2026aAppliquez des techniques de Deep Learning pour débruiter les signaux. Utilisez des transformées temps-fréquence différenciables pour reconstruire les signaux lorsqu’il manque des informations.
Applications
| Signal Analyzer | Visualiser et comparer plusieurs signaux et spectres |
| Regression Learner | Train regression models to predict data using supervised machine learning |
| Experiment Manager | Create and run experiments to train and compare deep learning networks |
Fonctions
Informations connexes
Rubriques
- Use Experiment Manager Templates for Signal Processing Workflows
Set up and run deep learning experiments for signal segmentation, classification, and regression.
Sélection d՚exemples
Denoise Signals with Generative Adversarial Networks
Use autoencoders and generative adversarial networks to denoise signals.
CBRS Band Radar Parameter Estimation Using YOLOX
Detect radar pulses in noise and estimate the pulse parameters using a combination of time-frequency maps and a deep-learning object detector.
Signal Source Separation Using W-Net Architecture
Use a deep learning network to separate two mixed signal sources.
Denoise EEG Signals Using Differentiable Signal Processing Layers
Remove EOG noise from EEG signals using deep learning regression.
Human Health Monitoring Using Continuous Wave Radar and Deep Learning
Use a deep learning network to reconstruct electrocardiograms from continuous-wave radar signals.
Denoise Speech Using Deep Learning Networks
Denoise speech signals using fully connected and convolutional neural networks.
Denoise Signals with Adversarial Learning Denoiser Model
Use an adversarial learning denoiser model to denoise signals.
Signal Recovery with Differentiable Scalograms and Spectrograms
Use differentiable time-frequency transforms and gradient descent to recover a time-domain signal without the need for phase information.
