Signal Processing, Audio, and Wavelet
Accelerate signal processing, audio processing, and wavelet analysis
applications
Use parallel computing to accelerate signal processing, audio processing, and wavelet analysis applications by using Parallel Computing Toolbox™ together with Signal Processing Toolbox™, Audio Toolbox™, Wavelet Toolbox™.
Apps
| Signal Feature Extractor | Extract and analyze signal features (Since R2025a) |
Topics
Signal Processing
- 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. (Since R2022b) - 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. (Since R2024b)
Audio
- 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. (Since R2024a) - 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.
Wavelet
- 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.
Related Information
- Functions with
gpuArraySupport (Signal Processing Toolbox) - Functions with
gpuArraySupport (Audio Toolbox) - Functions with
gpuArraySupport (Wavelet Toolbox)
Featured Examples
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.
