代码生成和GPU支持万博1manbetx

生成便携式C/C ++/MEX功能，并使用GPU部署或加速处理

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 thegpuArray(Parallel Computing Toolbox)功能以将数据传输到GPU，然后调用gather(Parallel Computing Toolbox)功能可从GPU检索输出数据。有关支持GPU上执行的音频工具箱函数列表，请参见万博1manbetxFunction List (gpuArraysupport)。你需要Parallel Computing Toolbox™ to enable GPU support.

C/C ++代码生成。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, seeFunction List (C/C++ Code Generation)。你需要MATLAB Coder™生成C/C ++代码。

GPU代码生成。After you develop your application, you can generate optimized CUDA^®NVIDIA的代码^®MATLAB代码的GPU。该代码可以作为源代码，静态库或动态库集成到您的项目中，并可用于GPU上的原型。您还可以在MATLAB中使用生成的CUDA代码来加速机器学习，深度学习或其他应用程序中MATLAB代码的计算密集型部分。有关支持GPU代码生成的音频工具箱函数列表，请参见万博1manbetx功能列表（GPU代码生成）。你需要MATLAB Coder和GPU CODER™生成CUDA代码。

Apps

MATLAB Coder	Generate C code or MEX function fromMATLAB代码
GPU编码器	Generate GPU code fromMATLAB代码

Functions

`代码gen`	从中生成C/C ++代码MATLAB代码
`gather`	将分布式阵列或GPUARRAY传输到本地工作区
`gpuArray`	Array stored on GPU

Topics

Generate C Code at the Command Line（MATLAB编码器）
通过使用MATLAB代码生成C/C ++代码代码gencommand.
Run MATLAB Functions on a GPU(Parallel Computing Toolbox)
如果您提供一个，MATLAB和其他工具箱中的数百个功能会自动在GPU上运行gpuArray(Parallel Computing Toolbox) argument.
与MATLAB编码器进行深度学习的先决条件（MATLAB编码器）
Install products and configure environment for code generation for deep learning networks.
释放的G万博1manbetxPU支持(Parallel Computing Toolbox)
Support for NVIDIA GPU architectures.
识别和显示口头指令在Android Device（万博1manbet万博1manbetxxsimulink支持包适用于Android设备）
此示例显示了如何为Android™设备使用Simulink®支持软件万博1manbetx万博1manbetx包来部署深度学习算法，该算法识别和显示通过您的Android设备（例如电话或平板电脑）使用的命令。

Related Information

从MATLAB代码生成C代码
GPU计算教程

Featured Examples

使用Simulink使用Intel MKL-DNN生成语音命令识别代码万博1manbetx

使用嵌入式编码器^®in Simulink^®and Intel^®MKL-DNN to deploy feature extraction and a convolutional neural network for speech command recognition on Intel processors.

开放型号

Speech Command Recognition on Raspberry Pi Using Simulink

在Raspberry Pi™上部署功能提取和卷积神经网络，以供语音命令识别。

开放型号

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.

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Keyword Spotting in Noise Code Generation on Raspberry Pi

使用双向长期记忆（BILSTM）网络和MEL频率CEPSTRAL系数（MFCC）功能提取在Raspberry Pi™上演示了用于关键字斑点的代码生成。具有深度学习支持的MATLAB®编码器™可以在Raspberry P万博1manbetxi上生成独立的可执行文件（.ELF）文件。MATLAB®（.MLX）文件与生成的可执行文件之间的通信发生在异步用户数据报协议（UDP）上。传入的语音信号使用时间尺寸显示。掩模显示为关键字的斑点实例的蓝色矩形，是的。有关MFCC功能提取和深度学习网络培训的更多详细信息，请使用MFCC和LSTM网络访问噪声中的关键字。

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使用英特尔MKL-DNN在噪声代码生成中发现关键字

Demonstrates code generation for keyword spotting using a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficient (MFCC) feature extraction. MATLAB® Coder™ with Deep Learning Support enables the generation of a standalone executable (.exe) file. Communication between the 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.

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Speech Command Recognition Code Generation with Intel MKL-DNN

在英特尔®处理器上部署功能提取和卷积神经网络（CNN）。为了生成功能提取和网络代码，您可以将MATLAB®编码器和Intel®Math-Matherel库用于深神经网络（MKL-DNN）。在此示例中，生成的代码是MATLAB可执行文件（MEX）函数，该函数由MATLAB脚本调用，该脚本将显示预测的语音命令以及时域信号和听觉频谱图。有关音频预处理和网络培训的详细信息，请参阅使用深度学习的语音命令识别。

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语音命令识别代码生成Raspberry Pi

Deploy feature extraction and a convolutional neural network (CNN) for speech command recognition to Raspberry Pi™. To generate the feature extraction and network code, you use MATLAB Coder™, MATLAB® Support Package for Raspberry Pi Hardware, and the ARM® Compute Library. In this example, the generated code is an executable on your Raspberry Pi, which is called by a MATLAB script that displays the predicted speech command along with the signal and auditory spectrogram. Interaction between the MATLAB script and the executable on your Raspberry Pi is handled using the user datagram protocol (UDP). For details about audio preprocessing and network training, see Speech Command Recognition Using Deep Learning.

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基于声学的机器故障识别代码生成Intel MKL-DNN

Generate a MATLAB standalone executable for acoustics-based machine fault recognition.

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基于声学的机器故障识别代码生成Raspberry Pi

为基于声学的机器故障识别生成Raspberry Pi独立可执行文件。

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