Classification
Classifiez les signaux avec des workflows d’IA centrés sur les données.
Applications
| Classification Learner | Train models to classify data using supervised machine learning |
| Experiment Manager | Design and run experiments to train and compare deep learning networks |
Fonctions
Informations connexes
- Prétraitement et extraction de caractéristiques
- Classification de séquences avec le Deep Learning (Deep Learning Toolbox)
Rubriques
- Use Experiment Manager Templates for Signal Processing Workflows
Set up and run deep learning experiments for signal segmentation, classification, and regression.
- Learn Pre-Emphasis Filter Using Deep Learning
Use a convolutional deep network to learn a pre-emphasis filter for speech recognition. (depuis R2021b)
Sélection d՚exemples
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.
Direction-of-Arrival Estimation Using Deep Learning
Estimate direction of arrival using deep learning by predicting angular directions directly from the sample covariance matrix.
Classify ECG Signals Using Long Short-Term Memory Networks
Classify heartbeat electrocardiogram data using deep learning and signal processing.
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.
Hand Gesture Classification Using Radar Signals and Deep Learning
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network.
Musical Instrument Classification with Joint Time-Frequency Scattering
Classify musical instruments using joint time-frequency features paired with a 3-D convolutional network.
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.
Classify Time Series Using Wavelet Analysis and Deep Learning
Classify ECG signals using the continuous wavelet transform and a deep convolutional neural network.
Wavelet Time Scattering Classification of Phonocardiogram Data
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.
Export Labeled Data from Signal Labeler for Deep Learning Classification
Label signals and export data using Signal Labeler to train a deep learning classifier.
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.
Acoustic Scene Classification with Wavelet Scattering
Use wavelet scattering and joint time-frequency scattering with a support vector machine to classify urban environments by sound.
Air Compressor Fault Detection Using Wavelet Scattering
Classify faults in acoustic recordings of air compressors using a wavelet scattering network and a support vector machine.
Pedestrian and Bicyclist Classification Using Deep Learning
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis.
Spoken Digit Recognition with Wavelet Scattering and Deep Learning
Classify spoken digits using both machine learning and deep learning techniques.
Time-Frequency Convolutional Network for EEG Data Classification
Classify electroencephalographic (EEG) time series from persons with and without epilepsy.
Signal Classification Using Wavelet-Based Features and Support Vector Machines
Classify electrocardiogram signals using features derived from wavelets and an autoregressive model.
Radar and Communications Waveform Classification Using Deep Learning
Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).
Signal Classification Using Wavelet-Based Features and Support Vector Machines
Classify electrocardiogram signals using features derived from wavelets and an autoregressive model.
