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信号源分离、去噪、信号恢复

使用深度学习方法对信号进行去噪。当存在缺失信息时，使用可微分时频变换来重新构造信号。

函数

deepSignalAnomalyDetector Create signal anomaly detector (自 R2023a 起)

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

`cwtmag2sig`	Signal reconstruction from CWT magnitude (自 R2023b 起)
`dlcwt`	Deep learning continuous wavelet transform (自 R2022b 起)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (自 R2022a 起)
`dlstft`	Deep learning short-time Fourier transform (自 R2021a 起)
`dlistft`	Deep learning inverse short-time Fourier transform (自 R2024a 起)
`cwtfilterbank`	Continuous wavelet transform filter bank
`findchangepts`	Find abrupt changes in signal
`findpeaks`	查找局部最大值
`modwt`	Maximal overlap discrete wavelet transform
`risetime`	Rise time of positive-going bilevel waveform transitions
`stft`	Short-time Fourier transform
`stftmag2sig`	Signal reconstruction from STFT magnitude (自 R2020b 起)
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction (自 R2021b 起)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (自 R2021a 起)
`waveletScattering`	Wavelet time scattering

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

Model wavelet scattering network in Simulink (自 R2022b 起)

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.