회귀

신호 소스 분리, 잡음 제거, 신호 복원

딥러닝 기법을 사용하여 신호의 잡음을 제거합니다. 누락된 정보가 있을 때 미분 가능한 시간-주파수 변환을 사용하여 신호를 복원합니다.

함수

deepSignalAnomalyDetector Create signal anomaly detector (R2023a 이후)

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

`cwtmag2sig`	Signal reconstruction from CWT magnitude (R2023b 이후)
`dlcwt`	Deep learning continuous wavelet transform (R2022b 이후)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (R2022a 이후)
`dlstft`	Deep learning short-time Fourier transform (R2021a 이후)
`dlistft`	Deep learning inverse short-time Fourier transform (R2024a 이후)
`cwtfilterbank`	Continuous wavelet transform filter bank
`findchangepts`	신호 내 급격한 변화 찾기
`findpeaks`	국소 최댓값 구하기
`modwt`	Maximal overlap discrete wavelet transform
`risetime`	Rise time of positive-going bilevel waveform transitions
`stft`	단시간 푸리에 변환
`stftmag2sig`	Signal reconstruction from STFT magnitude (R2020b 이후)
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction (R2021b 이후)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (R2021a 이후)
`waveletScattering`	Wavelet time scattering

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

Model wavelet scattering network in Simulink (R2022b 이후)

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.