전처리 및 특징 추출

시간 영역, 주파수 영역 및 시간-주파수 영역에서 신호 특징 추출

명령줄 함수 또는 특징 추출기 앱을 사용하여 머신러닝 모델에 사용할 유의미한 신호 특징을 추출합니다.

앱

EDF 파일 분석기EDF 파일 또는 EDF+ 파일 보기 (R2021a 이후)
신호 분석기여러 개의 신호와 스펙트럼을 시각화하여 비교
신호 레이블 지정기관심 있는 신호 특성, 신호 영역 및 신호 지점에 레이블 지정하기
신호 특징 추출기Extract and analyze signal features (R2025a 이후)

함수

모두 확장

detrend다항식 추세 제거
envelope신호 포락선
lowpass신호에 저역통과 필터 적용하기
smoothdata잡음 있는 데이터의 평활화
cwtmag2sigSignal reconstruction from CWT magnitude (R2023b 이후)
stftmag2sigSignal reconstruction from STFT magnitude
cwtfilterbankContinuous wavelet transform filter bank
findpeaks국소 최댓값 구하기
findchangepts신호 내 급격한 변화 찾기
modwtMaximal overlap discrete wavelet transform
risetime Rise time of positive-going bilevel waveform transitions
stft단시간 푸리에 변환
signalTimeFeatureExtractorStreamline signal time feature extraction (R2021a 이후)
signalFrequencyFeatureExtractorStreamline signal frequency feature extraction (R2021b 이후)
signalTimeFrequencyFeatureExtractorStreamline signal time-frequency feature extraction (R2024a 이후)
timeFrequencyScatteringJoint time-frequency scattering (R2024b 이후)
waveletScatteringWavelet time scattering
audioDatastoreDatastore for collection of audio files
arrayDatastoreDatastore for in-memory data
imageDatastore이미지 데이터의 데이터저장소
signalDatastoreDatastore for collection of signals
edfheaderCreate header structure for EDF or EDF+ file (R2021a 이후)
edfinfoGet information about EDF/EDF+ file
edfreadEDF/EDF+ 파일에서 데이터 읽기
edfwriteCreate or modify EDF or EDF+ file (R2021a 이후)
paddataPad data by adding elements (R2023b 이후)
resizeResize data by adding or removing elements (R2023b 이후)
trimdataTrim data by removing elements (R2023b 이후)
dlcwtDeep learning continuous wavelet transform (R2022b 이후)
dlicwtDeep learning inverse continuous 1-D wavelet transform (R2024b 이후)
dldwtDifferentiable discrete wavelet transform (R2025a 이후)
dlidwtDifferentiable inverse discrete wavelet transform (R2025a 이후)
dlmodwtDeep learning maximal overlap discrete wavelet transform and multiresolution analysis (R2022a 이후)
dlstftDeep learning short-time Fourier transform (R2021a 이후)
dlistftDeep learning inverse short-time Fourier transform (R2024a 이후)
cwtLayerContinuous wavelet transform layer (R2022b 이후)
icwtLayerInverse continuous wavelet transform layer (R2024b 이후)
modwtLayerMaximal overlap discrete wavelet transform layer (R2022b 이후)
stftLayerShort-time Fourier transform layer (R2021b 이후)
istftLayerInverse short-time Fourier transform layer (R2024a 이후)

관련 정보

도움말 항목

추천 예제

Machine Learning and Deep Learning Classification Using Signal Feature Extraction Objects

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.

Feature Selection Based on Deep Learning Interpretability for Signal Classification Applications

Feature Selection Based on Deep Learning Interpretability for Signal Classification Applications

Use the locally interpretable model-agnostic explanation technique to interpret decision-making processes of a deep learning network.

Human Activity Recognition Using Signal Feature Extraction and Machine Learning

Human Activity Recognition Using Signal Feature Extraction and Machine Learning

Extract features from smartphone sensor signals and use them to classify human activity.

(Signal Processing Toolbox)

  • R2021b 이후
Time-Frequency Feature Embedding with Deep Metric Learning

Time-Frequency Feature Embedding with Deep Metric Learning

Construct wavelet-based feature embeddings to classify EEG signals from persons with and without epilepsy.

Signal Classification Using Wavelet-Based Features and Support Vector Machines

Signal Classification Using Wavelet-Based Features and Support Vector Machines

Classify electrocardiogram signals using features derived from wavelets and an autoregressive model.

(Wavelet Toolbox)