신호에 대한 머신러닝 및 딥러닝

신호 레이블 지정, 특징 엔지니어링, 데이터셋 생성

Signal Processing Toolbox™는 머신러닝 및 딥러닝 워크플로를 위해 신호 레이블 지정, 특징 엔지니어링 및 데이터셋 생성을 수행하는 기능을 제공합니다.

앱

신호 분석기	여러 개의 신호와 스펙트럼을 시각화하여 비교
신호 레이블 지정기	관심 있는 신호 특성, 신호 영역 및 신호 지점에 레이블 지정하기
EDF 파일 분석기	EDF 파일 또는 EDF+ 파일 보기

`labeledSignalSet`	Create labeled signal set
`signalLabelDefinition`	Create signal label definition
`countlabels`	Count number of unique labels
`folders2labels`	Get list of labels from folder names
`splitlabels`	Find indices to split labels according to specified proportions
`signalMask`	Modify and convert signal masks and extract signal regions of interest
`binmask2sigroi`	Convert binary mask to matrix of ROI limits
`extendsigroi`	Extend signal regions of interest to left and right
`extractsigroi`	Extract signal regions of interest
`mergesigroi`	Merge signal regions of interest
`removesigroi`	Remove signal regions of interest
`shortensigroi`	Shorten signal regions of interest from left and right
`sigroi2binmask`	Convert matrix of ROI limits to binary mask

`edfinfo`	Get information about EDF/EDF+ file
`edfwrite`	Create or modify EDF or EDF+ file
`edfheader`	为法国电力公司(EDF)或法国电力公司(EDF) +文件创建头结构
`edfread`	Read data from EDF/EDF+ file
`signalDatastore`	Datastore for collection of signals

`dlstft`	Deep learning short-time Fourier transform
`stftLayer`	Short-time Fourier transform layer
`findchangepts`	신호 내 급격한 변화 찾기
`findpeaks`	국소 최댓값 구하기
`findsignal`	Find signal location using similarity search
`fsst`	Fourier synchrosqueezed transform
`instbw`	Estimate instantaneous bandwidth
`instfreq`	순시 주파수 추정
`pentropy`	신호의 스펙트럼 엔트로피
`periodogram`	주기도 파워 스펙트럼 밀도 추정값
`pkurtosis`	Spectral kurtosis from signal or spectrogram
`powerbw`	전력대역폭
`pspectrum`	주파수 영역과 시간-주파수 영역의 신호 분석
`pwelch`	Welch의 파워 스펙트럼 밀도 추정값
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction
`signalTimeFeatureExtractor`	Streamline signal time feature extraction
`zerocrossrate`	Zero-crossing rate

Choose an App to Label Ground Truth Data
Decide which app to use to label ground truth data:Image Labeler,Video Labeler,Ground Truth Labeler,Lidar Labeler, orSignal Labeler.
Radar and Communications Waveform Classification Using Deep Learning(Phased Array System Toolbox)
This example shows how to classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).
Pedestrian and Bicyclist Classification Using Deep Learning(Radar Toolbox)
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.
Music Genre Classification Using Wavelet Time Scattering(Wavelet Toolbox)
Classify the genre of a musical excerpt using wavelet time scattering and the audio datastore.
Wavelet Time Scattering Classification of Phonocardiogram Data(Wavelet Toolbox)
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.
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.