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Traitement du signal avec Deep Learning
Appliquez le Deep Learning au traitement du signal en utilisant Deep Learning Toolbox™ avec Signal Processing Toolbox™, Wavelet Toolbox™, Radar Toolbox ou DSP System Toolbox™. Pour des applications de traitement audio et de la parole, veuillez consulter Traitement audio avec Deep Learning. Pour des applications de télécommunications, veuillez consulter Télécommunications avec Deep Learning.
Applications
Signal Labeler | Label signal attributes, regions, and points of interest, and extract features |
Fonctions
Blocs
Wavelet Scattering | Model wavelet scattering network in Simulink (depuis R2022b) |
Rubriques
- 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.
- Maritime Clutter Suppression with Neural Networks (Radar Toolbox)
Train and evaluate a convolutional neural network to remove clutter returns from maritime radar PPI images using the Deep Learning Toolbox™.
- Signal Recovery with Differentiable Scalograms and Spectrograms (Signal Processing Toolbox)
Use differentiable time-frequency transforms to recover a time-domain signal without the need for phase information or transform inversion.
- Signal Source Separation Using W-Net Architecture (Signal Processing Toolbox)
Use a deep learning network to separate two mixed signal sources.
- Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox)
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis.
- Radar and Communications Waveform Classification Using Deep Learning (Radar Toolbox)
Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).
- Label Radar Signals with Signal Labeler (Radar Toolbox)
Label the time and frequency features of pulse radar signals with added noise.
- Radar Target Classification Using Machine Learning and Deep Learning (Radar Toolbox)
Classify radar returns using machine and deep learning approaches.
- Automate Signal Labeling with Custom Functions (Signal Processing Toolbox)
Use Signal Labeler to locate and label QRS complexes and R peaks of ECG signals.
- Crack Identification from Accelerometer Data (Wavelet Toolbox)
Use wavelet and deep learning techniques to detect transverse pavement cracks and localize their position.
- Create Labeled Signal Sets Iteratively with Reduced Human Effort (Signal Processing Toolbox)
Use deep learning to decrease the human effort required to label signals.
- Label Signal Attributes, Regions of Interest, and Points (Signal Processing Toolbox)
Use Signal Labeler to label attributes, regions, and points of interest in a set of whale songs.
- Automate Signal Labeling with Custom Functions (Signal Processing Toolbox)
Use Signal Labeler to locate and label QRS complexes and R peaks of ECG signals.
- 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.
- GPU Acceleration of Scalograms for Deep Learning (Wavelet Toolbox)
Use your GPU to accelerate feature extraction for signal classification.
- Denoise EEG Signals Using Differentiable Signal Processing Layers (Signal Processing Toolbox)
Remove EOG noise from EEG signals using deep learning regression.