分类

对信号属性进行分类，通过序列到序列分类执行信号分割

使用信号分类的时频方法派生特征。

函数

`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）

`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）
`cwtfilterbank`	Continuous wavelet transform filter bank
`findchangepts`	Find abrupt changes in signal
`findpeaks`	Find local maxima
`modwt`	Maximal overlap discrete wavelet transform
`risetime`	Rise time of positive-going bilevel waveform transitions
`stft`	Short-time Fourier transform （自 R2019a 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）

Classify Arm Motions Using EMG Signals and Deep Learning (Signal Processing Toolbox)
Classify arm motions using labeled EMG signals and a long short-term memory network.
Detect Air Compressor Sounds in Simulink Using Wavelet Scattering (DSP System Toolbox)
Use the Wavelet Scattering block and a pretrained deep learning network to classify audio signals.
Detect Anomalies in ECG Time-Series Data Using Wavelet Scattering and LSTM Autoencoder in Simulink (DSP System Toolbox)
Use wavelet scattering and deep learning network to detect anomalies in ECG signals.
Manage Data Sets for Machine Learning and Deep Learning Workflows (Signal Processing Toolbox)
Organize, access, and manage data sets for different AI applications.