Regresión

Separación de fuente de señal, eliminación de ruido y recuperación de señal

Utilice técnicas de deep learning para eliminar ruidos de las señales. Utilice transformadas de tiempo-frecuencia diferenciables para reconstruir señales en las que falte información.

Funciones

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Codificadores automáticos

deepSignalAnomalyDetector Create signal anomaly detector (desde R2023a)

Capas de procesamiento de señales

`cwtLayer`	Continuous wavelet transform (CWT) layer (desde R2022b)
`modwtLayer`	Maximal overlap discrete wavelet transform (MODWT) layer (desde R2022b)
`stftLayer`	Short-time Fourier transform layer (desde R2021b)
`istftLayer`	Inverse short-time Fourier transform layer (desde R2024a)

Extracción de características

`cwtmag2sig`	Signal reconstruction from CWT magnitude (desde R2023b)
`dlcwt`	Deep learning continuous wavelet transform (desde R2022b)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (desde R2022a)
`dlstft`	Deep learning short-time Fourier transform (desde R2021a)
`dlistft`	Deep learning inverse short-time Fourier transform (desde R2024a)
`cwtfilterbank`	Continuous wavelet transform filter bank
`findchangepts`	Find abrupt changes in signal
`findpeaks`	Encontrar los máximos locales
`modwt`	Maximal overlap discrete wavelet transform
`risetime`	Rise time of positive-going bilevel waveform transitions
`stft`	Transformada de Fourier de tiempo corto
`stftmag2sig`	Signal reconstruction from STFT magnitude (desde R2020b)
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction (desde R2021b)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (desde R2021a)
`waveletScattering`	Wavelet time scattering

Gestión de datos y almacenes de datos

`edfheader`	Create header structure for EDF or EDF+ file (desde R2021a)
`edfinfo`	Get information about EDF/EDF+ file (desde R2020b)
`edfread`	Leer datos del archivo EDF/EDF+ (desde R2020b)
`edfwrite`	Create or modify EDF or EDF+ file (desde R2021a)
`paddata`	Pad data by adding elements (desde R2023b)
`resize`	Resize data by adding or removing elements (desde R2023b)
`trimdata`	Trim data by removing elements (desde R2023b)
`signalDatastore`	Datastore for collection of signals (desde R2020a)

Bloques

Wavelet Scattering

Model wavelet scattering network in Simulink (desde R2022b)

Temas

Manage Data Sets for Machine Learning and Deep Learning Workflows (Signal Processing Toolbox)
Organize, access, and manage data sets for different AI applications.
Signal Recovery with Differentiable Scalograms and Spectrograms (Wavelet Toolbox)
Use differentiable time-frequency transforms and gradient descent to recover a time-domain signal without the need for phase information.
Signal Source Separation Using W-Net Architecture (Signal Processing Toolbox)
Use a deep learning network to separate two mixed signal sources.
Denoise EEG Signals Using Differentiable Signal Processing Layers (Signal Processing Toolbox)
Remove EOG noise from EEG signals using deep learning regression.

Ejemplos destacados

Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences

Detect anomalies in acoustic data using wavelet scattering with the deepSignalAnomalyDetector object.

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Anomaly Detection Using Autoencoder and Wavelets

Use wavelet-extracted features and an autoencoder to detect arc signals in a DC system.

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Detect Anomalies Using Wavelet Scattering with Autoencoders

Learn how to develop an alert system for predictive maintenance using wavelet scattering and deep learning.

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Denoise Speech Using Deep Learning Networks

Denoise speech signals using deep learning networks. The example compares two types of networks applied to the same task: fully connected, and convolutional.

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