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信号的机器学习和深度学习延伸

信号标注、特征工程、数据集生成

Signal Processing Toolbox™ 为机器学习和深度学习工作流提供执行信号标注、特征工程和数据集生成的功能。

App

Signal AnalyzerVisualize and compare multiple signals and spectra
Signal LabelerLabel signal attributes, regions, and points of interest

函数

全部展开

labeledSignalSetCreate labeled signal set
signalLabelDefinitionCreate signal label definition
signalDatastoreDatastore for collection of signals
findchangeptsFind abrupt changes in signal
findpeaksFind local maxima
findsignalFind signal location using similarity search
fsstFourier synchrosqueezed transform
instfreqEstimate instantaneous frequency
pentropySpectral entropy of signal
periodogramPeriodogram power spectral density estimate
pkurtosisSpectral kurtosis from signal or spectrogram
powerbwPower bandwidth
pspectrumAnalyze signals in the frequency and time-frequency domains
pwelchWelch’s power spectral density estimate

主题

Radar Waveform Classification Using Deep Learning (Phased Array System Toolbox)

This example shows how to classify radar waveform types of generated synthetic data using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

Pedestrian and Bicyclist Classification Using Deep Learning (Phased Array System Toolbox)

This example shows how to classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.

Classify Time Series Using Wavelet Analysis and Deep Learning

Classify ECG signals using the continuous wavelet transform and a deep convolutional neural network.

相关信息

在 MATLAB 中进行深度学习 (Deep Learning Toolbox)

使用深度学习进行序列分类 (Deep Learning Toolbox)

特色示例