Regressione
Utilizzare le tecniche di Deep Learning per ridurre il rumore nei segnali. Utilizzare le trasformate differenziabili tempo-frequenza per ricostruire i segnali in presenza di informazioni mancanti.
Funzioni
Blocchi
| Wavelet Scattering | Model wavelet scattering network in Simulink (Da R2022b) |
Argomenti
- Denoise Signals with Generative Adversarial Networks
Use autoencoders and generative adversarial networks to denoise signals.
- Manage Data Sets for Machine Learning and Deep Learning Workflows (Signal Processing Toolbox)
Organize, access, and manage data sets for different AI applications.
- Signal Recovery with Differentiable Scalograms and Spectrograms (Wavelet Toolbox)
Use differentiable time-frequency transforms and gradient descent to recover a time-domain signal without the need for phase information. (Da R2022b)
- Signal Source Separation Using W-Net Architecture (Signal Processing Toolbox)
Use a deep learning network to separate two mixed signal sources. (Da R2022b)
- Denoise EEG Signals Using Differentiable Signal Processing Layers (Signal Processing Toolbox)
Remove EOG noise from EEG signals using deep learning regression. (Da R2021b)
Esempi in primo piano
Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
Detect anomalies in acoustic data using wavelet scattering with the
deepSignalAnomalyDetector object.
Detect Anomalies Using Wavelet Scattering with Autoencoders
Learn how to develop an alert system for predictive maintenance using wavelet scattering and deep learning.
Denoise Speech Using Deep Learning Networks
Denoise speech signals using deep learning networks. The example compares two types of networks applied to the same task: fully connected, and convolutional.
