Code Generation and GPU Support
Audio Toolbox™ includes support to accelerate prototyping in MATLAB® and to generate code for deployment.
GPU Code Acceleration. To speed up your code while prototyping, Audio Toolbox includes functions that can execute on a Graphics
Processing Unit (GPU). You can use the gpuArray (Parallel Computing Toolbox) function
to transfer data to the GPU and then call the gather (Parallel Computing Toolbox) function to
retrieve the output data from the GPU. For a list of Audio Toolbox functions that support execution on GPUs, see Function List
(gpuArray support). You need
Parallel Computing Toolbox™ to enable GPU support.
C/C++ Code Generation. After you develop your application, you can generate portable C/C++ source code, standalone executables, or standalone applications from your MATLAB code. C/C++ code generation enables you to run your simulation on machines that do not have MATLAB installed and to speed up processing while you work in MATLAB. For a list of Audio Toolbox functions that support C/C++ code generation, see Function List (C/C++ Code Generation). You need MATLAB Coder™ to generate C/C++ code.
GPU Code Generation. After you develop your application, you can generate optimized CUDA® code for NVIDIA® GPUs from MATLAB code. The code can be integrated into your project as source code, static libraries, or dynamic libraries, and can be used for prototyping on GPUs. You can also use the generated CUDA code within MATLAB to accelerate computationally intensive portions of your MATLAB code in machine learning, deep learning, or other applications. For a list of Audio Toolbox functions that support GPU code generation, see Function List (GPU Code Generation). You need MATLAB Coder and GPU Coder™ to generate CUDA code.
Apps
| MATLAB Coder | Generate C code or MEX function from MATLAB code |
| GPU Coder | Generate CUDA code from MATLAB code |
Functions
Topics
GPU Code Acceleration
- Run MATLAB Functions on a GPU (Parallel Computing Toolbox)
Supply agpuArrayargument to automatically run functions on a GPU. - GPU Computing Requirements (Parallel Computing Toolbox)
Support for NVIDIA GPU architectures. - Measure and Improve GPU Performance (Parallel Computing Toolbox)
Time code running on your GPU and explore options for improving performance.
Code Generation
- Generate C Code at the Command Line (MATLAB Coder)
Use command line tools to generate C code from an example MATLAB function. - Prerequisites for Deep Learning with MATLAB Coder (MATLAB Coder)
Install products and configure environment for code generation for deep learning networks. - Recognize and Display Spoken Commands on Android Device (Simulink)
This example shows how to use the Simulink® Support Package for Android® Devices to deploy a deep learning algorithm that recognizes and displays commands spoken through your Android device such as a phone or tablet. - Deploy Smart Speaker Model on Raspberry Pi
Perform code generation and deploy Simulink® smart speaker model on Raspberry Pi® hardware.
Related Information
Featured Examples
Compress Machine Fault Recognition Neural Network Using Projection
Compress a pretrained acoustics-based machine fault recognition neural network using projection and principal component analysis.
Audio Event Classification Using TensorFlow Lite on Raspberry Pi
Perform audio event classification on Raspberry Pi® using the YAMNet pretrained deep neural network from the TensorFlow™ Lite library.
Speech Command Recognition Code Generation on Raspberry Pi
Generate code and deploy feature extraction and speech command recognition network on Raspberry Pi hardware.
Speech Command Recognition Code Generation with Intel MKL-DNN
Deploy feature extraction and a convolutional neural network (CNN) for speech command recognition on Intel® processors. To generate the feature extraction and network code, you use MATLAB® Coder™ and the Intel® Math Kernel Library for Deep Neural Networks (MKL-DNN). In this example, the generated code is a MATLAB executable (MEX) function, which is called by a MATLAB script that displays the predicted speech command along with the time domain signal and auditory spectrogram. For details about audio preprocessing and network training, see Train Deep Learning Network for Speech Command Recognition.
Speech Command Recognition on Raspberry Pi Using Simulink
Deploy feature extraction and a convolutional neural network for speech command recognition on Raspberry Pi.
Speech Command Recognition Code Generation Using Simulink
Use Embedded Coder® in Simulink and Intel® MKL-DNN to deploy feature extraction and a convolutional neural network for speech command recognition on Intel processors.
Accelerate Audio Machine Learning Workflows Using a GPU
Use GPU computing to accelerate machine learning workflows for audio, speech, and acoustic applications.
Accelerate Audio Deep Learning Using GPU-Based Feature Extraction
Leverage GPUs for feature extraction to decrease the time required to train an audio deep learning model.
Keyword Spotting in Noise Code Generation on Raspberry Pi
Demonstrates code generation for keyword spotting using a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficient (MFCC) feature extraction on Raspberry Pi®. MATLAB® Coder™ with Deep Learning Support enables the generation of a standalone executable (.elf) file on Raspberry Pi. Communication between MATLAB (.mlx) file and the generated executable file occurs over asynchronous User Datagram Protocol (UDP). The incoming speech signal is displayed using a timescope. A mask is shown as a blue rectangle surrounding spotted instances of the keyword, YES. For more details on MFCC feature extraction and deep learning network training, visit Keyword Spotting in Noise Using MFCC and LSTM Networks.
Keyword Spotting in Noise Code Generation with Intel MKL-DNN
Generate code to spot keywords using a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficient (MFCC) feature extraction.
Acoustics-Based Machine Fault Recognition Code Generation
Generate a MATLAB standalone executable for acoustics-based machine fault recognition.
Acoustics-Based Machine Fault Recognition Code Generation on Raspberry Pi
Generate a Raspberry Pi standalone executable for acoustics-based machine fault recognition.
