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Applications de l'IA
Audio, biomédecine, maintenance prédictive, radars et télécommunications
Intégrez des techniques de traitement du signal et de Deep Learning dans des applications concrètes comme la reconnaissance vocale, la classification d’électrocardiogrammes et le débruitage d’électroencéphalogrammes.
Rubriques
Audio
- Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
Detect anomalies in acoustic data using wavelet scattering and thedeepSignalAnomalyDetectorobject. (depuis R2024a) - Spoken Digit Recognition with Custom Log Spectrogram Layer and Deep Learning
Classify spoken digits using a deep convolutional neural network and a custom spectrogram layer. - 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. - Denoise Speech Using Deep Learning Networks
Denoise speech signals using fully connected and convolutional neural networks. - Acoustic Scene Classification with Wavelet Scattering (Wavelet Toolbox)
Use wavelet scattering and joint time-frequency scattering with a support vector machine to classify urban environments by sound. (depuis R2024b) - Musical Instrument Classification with Joint Time-Frequency Scattering (Wavelet Toolbox)
Classify musical instruments using joint time-frequency features paired with a 3-D convolutional network. (depuis R2024b) - Acoustic Scene Recognition Using Late Fusion (Wavelet Toolbox)
Create a multi-model late fusion system for acoustic scene recognition. - Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.
Biomédecine
- Human Health Monitoring Using Continuous Wave Radar and Deep Learning
Use a deep learning network to reconstruct electrocardiograms from continuous-wave radar signals. (depuis R2022b) - Human Activity Recognition Using Signal Feature Extraction and Machine Learning
Extract features from smartphone sensor signals and use them to classify human activity. (depuis R2021b) - Hand Gesture Classification Using Radar Signals and Deep Learning
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network. (depuis R2021b) - Classify ECG Signals Using Long Short-Term Memory Networks
Classify heartbeat electrocardiogram data using deep learning and signal processing. - Detect Anomalies in Signals Using deepSignalAnomalyDetector
Use autoencoders to detect abnormal points or segments in time series data. (depuis R2023a) - Waveform Segmentation Using Deep Learning
Segment human electrocardiogram signals using time-frequency analysis and deep learning. - Classify Arm Motions Using EMG Signals and Deep Learning
Classify arm motions using labeled EMG signals and a long short-term memory network. (depuis R2022a) - Denoise EEG Signals Using Differentiable Signal Processing Layers
Remove EOG noise from EEG signals using deep learning regression. (depuis R2021b) - Classify Time Series Using Wavelet Analysis and Deep Learning
Classify ECG signals using the continuous wavelet transform and a deep convolutional neural network. - Signal Source Separation Using W-Net Architecture
Use a deep learning network to separate two mixed signal sources. (depuis R2022b) - Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier. - Time-Frequency Convolutional Network for EEG Data Classification (Wavelet Toolbox)
Classify electroencephalographic (EEG) time series from persons with and without epilepsy. (depuis R2023a)
Bruit, vibrations et rudesse
- 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. (depuis R2024a) - Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
Detect anomalies in acoustic data using wavelet scattering and thedeepSignalAnomalyDetectorobject. (depuis R2024a) - Crack Identification from Accelerometer Data (Deep Learning Toolbox)
Use wavelet and deep learning techniques to detect and localize transverse pavement cracks. - Detect Anomalies Using Wavelet Scattering with Autoencoders (Deep Learning Toolbox)
Learn how to develop an alert system for predictive maintenance using wavelet scattering and deep learning. - Fault Detection Using Wavelet Scattering and Recurrent Deep Networks (Deep Learning Toolbox)
Classify faults in acoustic recordings of air compressors using a wavelet scattering network paired with a recurrent neural network.
Radars et télécommunications
- Automated Labeling of Time-Frequency Regions for AI-Based Spectrum Sensing Applications
Use rule-based methods or unsupervised learning techniques to help automate time-frequency data labeling. (depuis R2025a) - Export Labeled Data from Signal Labeler for AI-Based Spectrum Sensing Applications
Use deep learning networks and the Signal Labeler app to identify frames from the Bluetooth® and Wi-Fi® wireless standards. (depuis R2025a) - Wireless Resource Allocation Using Graph Neural Network
Use graph neural networks for power allocation in wireless networks. (depuis R2024b) - CBRS Band Radar Parameter Estimation Using YOLOX
Detect radar pulses in noise and estimates the pulse parameters using a combination of time-frequency maps and a deep-learning object detector. (depuis R2025a) - Direction-of-Arrival Estimation Using Deep Learning
Estimate direction of arrival using deep learning by predicting angular directions directly from the sample covariance matrix. (depuis R2025a) - Hand Gesture Classification Using Radar Signals and Deep Learning
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network. (depuis R2021b) - 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. - LPI Radar Waveform Classification Using Time-Frequency CNN (Radar Toolbox)
Train a time-frequency convolutional neural network (CNN) to classify received radar waveforms based on modulation scheme. (depuis R2024a) - Radar Target Classification Using Machine Learning and Deep Learning (Radar Toolbox)
Classify radar returns using machine and deep learning approaches. - Radar and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)
Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).
Informations connexes
- AI for Audio (Audio Toolbox)
- AI for Radar (Radar Toolbox)
- L’IA pour les télécommunications (Communications Toolbox)
