신호 처리, 오디오 및 웨이블릿
신호 처리, 오디오 처리 및 웨이블릿 분석 애플리케이션 가속화
Parallel Computing Toolbox™를 Signal Processing Toolbox™, Audio Toolbox™, Wavelet Toolbox™와 함께 사용하여 병렬 연산으로 신호 처리, 오디오 처리 및 웨이블릿 분석 애플리케이션을 가속화합니다.
앱
| 신호 특징 추출기 | Extract and analyze signal features (R2025a 이후) |
도움말 항목
신호 처리
- Classify ECG Signals Using Long Short-Term Memory Networks with GPU Acceleration (Signal Processing Toolbox)
Classify heartbeat electrocardiogram data using deep learning and signal processing with GPU acceleration. (R2022b 이후) - Accelerate Signal Feature Extraction and Classification Using a GPU (Signal Processing Toolbox)
Use a graphical processing unit (GPU) to extract signal multidomain features for bearing fault detection. (R2024b 이후)
오디오
- Extract Features from Audio Data Sets (Audio Toolbox)
Use different methods of extracting features from an audio data set. - Accelerate Audio Machine Learning Workflows Using a GPU (Audio Toolbox)
This example shows how to use GPU computing to accelerate machine learning workflows for audio, speech, and acoustic applications. (R2024a 이후) - Accelerate Audio Deep Learning Using GPU-Based Feature Extraction (Audio Toolbox)
Leverage GPUs for feature extraction to decrease the time required to train an audio deep learning model.
웨이블릿
- GPU Acceleration of Scalograms for Deep Learning (Wavelet Toolbox)
Use your GPU to accelerate feature extraction for ECG and spoken digit classification. - Wavelet Time Scattering with GPU Acceleration — Spoken Digit Recognition (Wavelet Toolbox)
Extract features on your GPU for signal classification.
관련 정보
gpuArray를 지원하는 함수 (Signal Processing Toolbox)gpuArray를 지원하는 함수 (Audio Toolbox)gpuArray를 지원하는 함수 (Wavelet Toolbox)
추천 예제
Accelerate Signal Feature Extraction and Classification Using a Parallel Pool of Workers
Use parallel computing to extract signal multidomain features for bearing fault detection.
Compare Speaker Separation Models
Compare the performance, size, and speed of multiple deep learning speaker separation models.
