Análisis de tiempo-frecuencia

Espectrograma, reasignación, sincrosqueezing, Wigner-Ville, marginales de tiempo-frecuencia, métodos de adaptación de datos

Signal Processing Toolbox™ proporciona funciones y apps que permiten visualizar y comparar el contenido de tiempo-frecuencia de las señales no estacionarias. Calcule la transformada de Fourier de tiempo corto y su inversa. Obtenga estimaciones espectrales nítidas mediante la reasignación o el sincrosqueezing de Fourier. Represente espectrogramas cruzados, distribuciones Wigner-Ville y espectros de persistencia. Extraiga y realice un seguimiento de las crestas de tiempo-frecuencia. Estime la frecuencia instantánea, el ancho de banda instantáneo, la curtosis del espectro y la entropía del espectro. Realice un análisis de tiempo-frecuencia adaptado a los datos mediante la descomposición de modo empírica o variacional y la transformada de Hilbert-Huang. Explore otras representaciones de tiempo-frecuencia y otros métodos de análisis con las funciones y las apps que se ofrecen en Wavelet Toolbox™.

Apps

Signal Analyzer	Visualizar y comparar múltiples señales y espectros
Signal Labeler	Etiquete atributos de señal, regiones y puntos de interés y extraiga características
Signal Multiresolution Analyzer	Decompose signals into time-aligned components
Wavelet Time-Frequency Analyzer	Visualize scalogram of signals (desde R2022a)

Funciones

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Transformadas

`fsst`	Fourier synchrosqueezed transform
`ifsst`	Inverse Fourier synchrosqueezed transform
`stft`	Transformada de Fourier de tiempo corto
`istft`	Inverse short-time Fourier transform
`dlstft`	Deep learning short-time Fourier transform (desde R2021a)
`dlistft`	Deep learning inverse short-time Fourier transform (desde R2024a)
`stftLayer`	Short-time Fourier transform layer (desde R2021b)
`istftLayer`	Inverse short-time Fourier transform layer (desde R2024a)
`stftmag2sig`	Signal reconstruction from STFT magnitude (desde R2020b)
`iscola`	Determine whether window-overlap combination is COLA compliant
`spectrogram`	Espectrograma utilizando la transformada de Fourier de tiempo corto
`xspectrogram`	Cross-spectrogram using short-time Fourier transforms
`pspectrum`	Analizar señales en los dominios de frecuencia y de tiempo-frecuencia
`kurtogram`	Visualize spectral kurtosis
`wvd`	Wigner-Ville distribution and smoothed pseudo Wigner-Ville distribution
`xwvd`	Cross Wigner-Ville distribution and cross smoothed pseudo Wigner-Ville distribution

Descriptores de espectro

`instbw`	Estimate instantaneous bandwidth (desde 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

Métodos de adaptación de datos

`emd`	Empirical mode decomposition
`ewt`	Empirical wavelet transform (desde R2020b)
`vmd`	Variational mode decomposition (desde R2020a)
`hht`	Hilbert-Huang transform

Análisis de tiempo-frecuencia con wavelets

`cqt`	Constant-Q nonstationary Gabor transform
`cwt`	Continuous 1-D wavelet transform
`modwpt`	Maximal overlap discrete wavelet packet transform
`modwt`	Maximal overlap discrete wavelet transform
`tqwt`	Tunable Q-factor wavelet transform (desde R2021b)
`waveletScattering`	Wavelet time scattering
`wcoherence`	Wavelet coherence and cross-spectrum
`wsst`	Wavelet synchrosqueezed transform

Temas

Spectrogram Computation with Signal Processing Toolbox
Compute and display spectrograms of signals using Signal Processing Toolbox functions.
Time-Frequency Gallery
Examine the features and limitations of the time-frequency analysis functions provided by Signal Processing Toolbox.
Practical Introduction to Time-Frequency Analysis Using the Continuous Wavelet Transform (Wavelet Toolbox)
Perform and interpret time-frequency analysis of signals using the continuous wavelet transform. (desde R2020a)
Practical Introduction to Multiresolution Analysis (Wavelet Toolbox)
Perform and interpret basic signal multiresolution analysis (MRA). (desde R2020a)
Wavelet Packet Harmonic Interference Removal (Wavelet Toolbox)
Use wavelet packets to remove harmonic interference from an electrocardiogram (ECG) signal. (desde R2021b)
Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox)
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis. (desde R2021a)
Radar and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)
Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).
Spectral Descriptors (Audio Toolbox)
Overview and applications of spectral descriptors.

Información relacionada

Time-Frequency Analysis (Wavelet Toolbox)

Ejemplos destacados

Classify ECG Signals Using Long Short-Term Memory Networks

Classify heartbeat electrocardiogram data using deep learning and signal processing.

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Waveform Segmentation Using Deep Learning

Segment human electrocardiogram signals using time-frequency analysis and deep learning.

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Spoken Digit Recognition with Custom Log Spectrogram Layer and Deep Learning

Classify spoken digits using a deep convolutional neural network and a custom spectrogram layer.

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Signal Recovery with Differentiable Scalograms and Spectrograms

Use differentiable time-frequency transforms to recover a time-domain signal without the need for phase information or transform inversion.

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Practical Introduction to Time-Frequency Analysis

Perform and interpret basic time-frequency signal analysis of nonstationary signals.

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Espectrograma cruzado de señales complejas

Resalte las regiones en las que dos señales comparten contenido frecuencial.

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Find Interference Using Persistence Spectrum

Use the persistence spectrum in Signal Analyzer app to detect and analyze brief events.

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Find and Track Ridges Using Reassigned Spectrogram

Use the reassigned spectrogram in Signal Analyzer to sharpen the time and frequency localization of spectrograms.

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