AI 应用
音频、生物医学、预测性维护、雷达和无线通信
将信号处理和深度学习方法集成到实际应用中，例如语音识别、心电图分类和脑电图去噪。
主题
音频
- Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
Detect anomalies in acoustic data using wavelet scattering and thedeepSignalAnomalyDetectorobject. (自 R2024a 起) - 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. (自 R2021a 起) - 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. - 使用深度学习网络对语音去噪
使用全连接和卷积神经网络对语音信号去噪。 - 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. (自 R2024b 起) - 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. (自 R2024b 起) - 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.
生物医学
- Human Health Monitoring Using Continuous Wave Radar and Deep Learning
Use a deep learning network to reconstruct electrocardiograms from continuous-wave radar signals. (自 R2022b 起) - Human Activity Recognition Using Signal Feature Extraction and Machine Learning
Extract features from smartphone sensor signals and use them to classify human activity. (自 R2021b 起) - Hand Gesture Classification Using Radar Signals and Deep Learning
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network. (自 R2021b 起) - 使用长短期记忆网络对 ECG 信号进行分类
使用深度学习和信号处理对心跳心电图数据进行分类。 - Detect Anomalies in Signals Using deepSignalAnomalyDetector
Use autoencoders to detect abnormal points or segments in time-series data. (自 R2023a 起) - 使用深度学习进行波形分割
使用时频分析和深度学习对人体心电图信号进行分割。 - Classify Arm Motions Using EMG Signals and Deep Learning
Classify arm motions using labeled EMG signals and a long short-term memory network. (自 R2022a 起) - Denoise EEG Signals Using Differentiable Signal Processing Layers
Remove EOG noise from EEG signals using deep learning regression. (自 R2021b 起) - 使用小波分析和深度学习对时间序列分类
使用连续小波变换和深度卷积神经网络对 ECG 信号进行分类。 - Signal Source Separation Using W-Net Architecture
Use a deep learning network to separate two mixed signal sources. (自 R2022b 起) - 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. (自 R2023a 起)
噪声、振动和粗糙度
- 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. (自 R2024a 起) - Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
Detect anomalies in acoustic data using wavelet scattering and thedeepSignalAnomalyDetectorobject. (自 R2024a 起) - Detect Anomalies in Machinery Using LSTM Autoencoder
Use a long short-term memory autoencoder to detect anomalies in data from an industrial machine. (自 R2023a 起) - 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.
雷达与无线通信
- 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. - 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. - Wireless Resource Allocation Using Graph Neural Network
Use graph neural networks for power allocation in wireless networks. (自 R2024b 起) - 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. - Hand Gesture Classification Using Radar Signals and Deep Learning
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network. (自 R2021b 起) - 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. (自 R2021a 起) - 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. (自 R2024a 起) - Radar Target Classification Using Machine Learning and Deep Learning (Radar Toolbox)
Classify radar returns using machine and deep learning approaches. (自 R2021a 起) - 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).
相关信息
- AI for Audio (Audio Toolbox)
- AI for Radar (Radar Toolbox)
- AI for Wireless (Communications Toolbox)
