회귀
딥러닝 기법을 적용하여 신호의 잡음을 제거합니다. 누락된 정보가 있을 때 미분 가능한 시간-주파수 변환을 사용하여 신호를 복원합니다.
함수
블록
| Wavelet Scattering | Simulink에서 웨이블릿 산란 신경망 모델링 (R2022b 이후) |
관련 정보
도움말 항목
- Use Experiment Manager Templates for Signal Processing Workflows (Signal Processing Toolbox)
Set up and run deep learning experiments for signal segmentation, classification, and regression.
- Signal Segmentation by Sweeping Hyperparameters (Signal Processing Toolbox)
- Signal Classification by Sweeping Hyperparameters (Signal Processing Toolbox)
- Signal Classification Using Transfer Learning (Signal Processing Toolbox)
- Signal Regression by Sweeping Hyperparameters (Signal Processing Toolbox)
추천 예제
CBRS Band Radar Parameter Estimation Using YOLOX
Detect radar pulses in noise and estimates the pulse parameters using a combination of time-frequency maps and a deep-learning object detector.
Denoise Signals with Generative Adversarial Networks
Use autoencoders and generative adversarial networks to denoise signals.
Signal Source Separation Using W-Net Architecture
Use a deep learning network to separate two mixed signal sources.
Denoise EEG Signals Using Differentiable Signal Processing Layers
Remove EOG noise from EEG signals using deep learning regression.
Human Health Monitoring Using Continuous Wave Radar and Deep Learning
Use a deep learning network to reconstruct electrocardiograms from continuous-wave radar signals.
딥러닝 신경망을 사용하여 음성 잡음 제거하기
이 예제에서는 딥러닝 신경망을 사용하여 음성 신호의 잡음을 제거하는 방법을 다룹니다. 이 예제에서는 동일한 작업에 적용된 두 가지 유형의 신경망, 즉 완전 연결 신경망과 컨벌루션 신경망을 비교합니다.
Denoise Signals with Adversarial Learning Denoiser Model
Use an adversarial learning denoiser model to denoise signals.
Signal Recovery with Differentiable Scalograms and Spectrograms
Use differentiable time-frequency transforms and gradient descent to recover a time-domain signal without the need for phase information.
