Pre-elaborazione ed estrazione delle feature

Estrarre le feature del segnale nei domini del tempo, della frequenza e del tempo-frequenza

Estrarre feature significative del segnale utilizzando le funzioni dalla riga di comando o l'app Signal Feature Extractor per impiegarle nei modelli di Machine Learning.

App

EDF File AnalyzerView EDF or EDF+ files
Signal AnalyzerVisualize and compare multiple signals and spectra
Signal LabelerLabel signal attributes, regions, and points of interest
Signal Feature ExtractorExtract and analyze signal features (Da R2025a)

Funzioni

espandi tutto

detrendRemove polynomial trend
envelopeSignal envelope
lowpassLowpass-filter signals
smoothdataSmooth noisy data
cwtmag2sigSignal reconstruction from CWT magnitude (Da R2023b)
stftmag2sigSignal reconstruction from STFT magnitude
cwtfilterbankContinuous wavelet transform filter bank
findpeaksFind local maxima
findchangeptsFind abrupt changes in signal
modwtMaximal overlap discrete wavelet transform
risetime Rise time of positive-going bilevel waveform transitions
stftShort-time Fourier transform
signalTimeFeatureExtractorStreamline signal time feature extraction
signalFrequencyFeatureExtractorStreamline signal frequency feature extraction (Da R2021b)
signalTimeFrequencyFeatureExtractorStreamline signal time-frequency feature extraction (Da R2024a)
timeFrequencyScatteringJoint time-frequency scattering (Da R2024b)
waveletScatteringWavelet time scattering
audioDatastoreDatastore for collection of audio files
arrayDatastoreDatastore for in-memory data
imageDatastoreDatastore for image data
signalDatastoreDatastore for collection of signals
edfheaderCreate header structure for EDF or EDF+ file
edfinfoGet information about EDF/EDF+ file
edfreadRead data from EDF/EDF+ file
edfwriteCreate or modify EDF or EDF+ file
paddataPad data by adding elements (Da R2023b)
resizeResize data by adding or removing elements (Da R2023b)
trimdataTrim data by removing elements (Da R2023b)
dlcwtDeep learning continuous wavelet transform (Da R2022b)
dlicwtDeep learning inverse continuous 1-D wavelet transform (Da R2024b)
dldwtDifferentiable discrete wavelet transform (Da R2025a)
dlidwtDifferentiable inverse discrete wavelet transform (Da R2025a)
dlmodwtDeep learning maximal overlap discrete wavelet transform and multiresolution analysis (Da R2022a)
dlstftDeep learning short-time Fourier transform
dlistftDeep learning inverse short-time Fourier transform (Da R2024a)
cwtLayerContinuous wavelet transform layer (Da R2022b)
icwtLayerInverse continuous wavelet transform layer (Da R2024b)
modwtLayerMaximal overlap discrete wavelet transform layer (Da R2022b)
stftLayerShort-time Fourier transform layer (Da R2021b)
istftLayerInverse short-time Fourier transform layer (Da R2024a)

Informazioni complementari

Argomenti

Esempi in primo piano

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)

  • Da R2021b
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)