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Vorverarbeitung und Merkmalsextraktion

Signalmerkmale im Zeit-, Frequenz- und Zeit-Frequenz-Bereich extrahieren

Extrahieren Sie aussagekräftige Signalmerkmale mithilfe von Kommandozeilenfunktionen oder der Signal Feature Extractor-App, um sie in Modellen des Machine Learnings zu verwenden.

Apps

EDF File Analyzer	View EDF or EDF+ files (Seit R2021a)
Signal Analyzer	Visualize and compare multiple signals and spectra
Signal Labeler	Label signal attributes, regions, and points of interest
Signal Feature Extractor	Extract and analyze signal features (Seit R2025a)

Funktionen

alle erweitern

Signalvorverarbeitung und Messungen

`detrend`	Remove polynomial trend
`envelope`	Signal envelope
`lowpass`	Lowpass-filter signals
`smoothdata`	Smooth noisy data
`cwtmag2sig`	Signal reconstruction from CWT magnitude (Seit R2023b)
`stftmag2sig`	Signal reconstruction from STFT magnitude
`islocalmax`	Find local maxima
`findchangepts`	Find abrupt changes in signal
`findpeaks`	Find local maxima
`refinepeaks`	Refine peak value and location estimates (Seit R2024b)
`zerocrossrate`	Zero-crossing rate (Seit R2021b)

Datastores und Datenmanagement

`audioDatastore`	Datastore for collection of audio files
`arrayDatastore`	Datastore for in-memory data
`imageDatastore`	Datastore for image data
`signalDatastore`	Datastore for collection of signals
`edfheader`	Create header structure for EDF or EDF+ file (Seit R2021a)
`edfinfo`	Get information about EDF/EDF+ file
`edfread`	Read data from EDF/EDF+ file
`edfwrite`	Create or modify EDF or EDF+ file (Seit R2021a)
`paddata`	Pad data by adding elements (Seit R2023b)
`resize`	Resize data by adding or removing elements (Seit R2023b)
`trimdata`	Trim data by removing elements (Seit R2023b)

Zeitbereichseigenschaften

`mean`	Durchschnitt oder Mittelwert eines Arrays
`rms`	Root mean square value
`std`	Standard deviation
`snr`	Signal-to-noise ratio
`thd`	Total harmonic distortion
`sinad`	Signal to noise and distortion ratio
`peak2rms`	Peak-magnitude-to-RMS ratio
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (Seit R2021a)
`timeScalarFeatureOptions`	Store information for converting time-domain feature vectors to scalar values (Seit R2024b)

Frequenzbereichsmerkmale

`meanfreq`	Mean frequency
`medfreq`	Median frequency
`bandpower`	Band power
`obw`	Occupied bandwidth
`powerbw`	Power bandwidth
`pwelch`	Welch’s power spectral density estimate
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction (Seit R2021b)
`frequencyScalarFeatureOptions`	Store information for converting frequency-domain feature vectors to scalar values (Seit R2024b)

Zeit-Frequenz-Transformationen

`stft`	Short-time Fourier transform
`fsst`	Fourier synchrosqueezed transform
`emd`	Empirical mode decomposition
`vmd`	Variational mode decomposition
`hht`	Hilbert-Huang transform
`cwt`	Continuous 1-D wavelet transform
`wsst`	Wavelet synchrosqueezed transform
`modwt`	Maximal overlap discrete wavelet transform
`modwpt`	Maximal overlap discrete wavelet packet transform

Zeit-Frequenz-Merkmale

`instbw`	Estimate instantaneous bandwidth (Seit R2021a)
`instfreq`	Estimate instantaneous frequency
`spectralCrest`	Spectral crest for signals and spectrograms
`spectralEntropy`	Spectral entropy for signals and spectrograms
`spectralFlatness`	Spectral flatness for signals and spectrograms
`spectralKurtosis`	Spectral kurtosis for signals and spectrograms
`spectralSkewness`	Spectral skewness for signals and spectrograms
`tfridge`	Time-frequency ridges
`signalTimeFrequencyFeatureExtractor`	Streamline signal time-frequency feature extraction (Seit R2024a)
`timeFrequencyScalarFeatureOptions`	Store information for converting time-frequency-domain feature vectors to scalar values (Seit R2024a)
`timeFrequencyScattering`	Joint time-frequency scattering (Seit R2024b)
`waveletScattering`	Wavelet time scattering
`wsstridge`	Time-frequency ridges from wavelet synchrosqueezing

Differenzierbare Signalverarbeitung

`dlcwt`	Deep learning continuous wavelet transform (Seit R2022b)
`dlicwt`	Deep learning inverse continuous 1-D wavelet transform (Seit R2024b)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (Seit R2022a)
`dlstft`	Deep learning short-time Fourier transform (Seit R2021a)
`dlistft`	Deep learning inverse short-time Fourier transform (Seit R2024a)
`cwtLayer`	Continuous wavelet transform layer (Seit R2022b)
`icwtLayer`	Inverse continuous wavelet transform layer (Seit R2024b)
`modwtLayer`	Maximal overlap discrete wavelet transform layer (Seit R2022b)
`stftLayer`	Short-time Fourier transform layer (Seit R2021b)
`istftLayer`	Inverse short-time Fourier transform layer (Seit R2024a)

Dienstprogramme zur Merkmalsextraktion

`framelbl`	Partition label sequence into frames (Seit R2024a)
`framesig`	Partition signal into frames (Seit R2024a)
`scalarFeatureOptions`	Store information for converting feature vectors to scalar values (Seit R2024a)

Themen

Use Signal Feature Extractor App

Extract and rank features from signals interactively. Prepare signal datasets for classification tasks.
- SCHRITT 1: Import Data into Signal Feature Extractor
- SCHRITT 2: Feature Extraction Using Signal Feature Extractor
- SCHRITT 3: Rank Features Using Signal Feature Extractor
- SCHRITT 4: Export Labeled Signal Sets and Features
Manage Data Sets for Machine Learning and Deep Learning Workflows
Organize, access, and manage data sets for different AI applications.
Accelerate Signal Feature Extraction and Classification Using a Parallel Pool of Workers
Use parallel computing to extract signal multidomain features for bearing fault detection. (Seit R2024b)
Accelerate Signal Feature Extraction and Classification Using a GPU
Use a graphical processing unit (GPU) to extract signal multidomain features for bearing fault detection. (Seit R2024b)
View, Preprocess, and Write EDF File
View and preprocess data stored in an EDF file.

Enthaltene Beispiele

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.

Seit R2024a
Live Script öffnen

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.

Seit R2024a
Live Script öffnen

Human Activity Recognition Using Signal Feature Extraction and Machine Learning

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

Seit R2021b
Live Script öffnen

Time-Frequency Feature Embedding with Deep Metric Learning

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

(Deep Learning Toolbox)

Signal Classification Using Wavelet-Based Features and Support Vector Machines

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

(Wavelet Toolbox)