Code Generation and Deployment

Generate standalone applications for desktop computers, mobile devices, and embedded targets

After you develop your application usingAudio Toolbox™features, you can generate portable C/C++ source code, standalone executables, or standalone applications from your MATLAB^®codeor Simulink^®model. With code generation, you can:

Run youralgorithmon machines that do not have MATLABor Simulinkinstalled.
Speed up processing while you work in the MATLABor Simulinkenvironment.
Embed your algorithm in hardware.
Incorporate your algorithms into legacy code.

This table compares deployment characteristics ofMATLAB Compiler™andMATLAB Coder™functionality. Use this table to decide how to deploy your MATLAB codeor Simulink model.

Deployment Characteristic	UsingMATLAB CompilerFunctionality	UsingMATLAB CoderFunctionality
Output	Nonreadable shared libraries.	Portable and readable C source code.
Library dependency	MATLABRuntime.	Library dependency only for audio I/O features:Run Audio I/O Features Outside MATLAB and Simulink.
Supported platforms for deployment	Windows^®,Mac, and Linux^®.	Any platform that supports ANSI^®/ISO^®C/C++ code.
Intellectual property protection	Packaged code is encrypted.	Code is not encrypted. You can protect your IP by generating shared libraries instead of readable code.
Algorithm acceleration	Same speed as MATLAB.	Potentially faster, depending on the algorithm.
MATLAB graphics	Supported.	Not supported.

To deploy an audio plugin to a digital audio workstation, seeExport a MATLAB Plugin to a DAW.

To deploy a System object™ audio plugin to Simulink, seeConvert Audio Plugin System Objects to Simulink Blocks.

Topics

What is C Code Generation from MATLAB?
Introduces code generation support.
Run Audio I/O Features Outside MATLAB and Simulink
Set your system environment to use dynamic library files for deployed audio I/O System objects and blocks.
识别和显示口头指令在Android Device(Simulink Support Package for Android Devices)
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.

Related Information

Active Noise Control: From Modeling to Real-Time Prototyping
Appropriate Tasks for MATLAB Compiler Products(MATLAB Compiler)
Standalone Applications(MATLAB Compiler)

Featured Examples

Speech Command Recognition Code Generation on 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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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 Speech Command Recognition Using Deep Learning.

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Parametric Audio Equalizer for Android Devices

Use the Single-Band Parametric EQ block and themultibandParametricEQSystem object™ from the Audio Toolbox™ to implement a parametric audio equalizer model. You can run the model on your host computer or deploy it to an Android device.

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Parametric Audio Equalizer for iOS Devices

Use the Single-Band Parametric EQ block and themultibandParametricEQSystem object™ to implement a parametric audio equalizer model, that can run on your host computer or an Apple iOS device.

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Audio Effects for iOS Devices

Use System objects™ from Audio Toolbox™ to implement echo and reverberation effects in a Simulink® model. You can run the model on your host computer or deploy it to an Apple iOS device.

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Multiband Dynamic Range Compression for iOS Devices

Use the Crossover Filter block andcompressorSystem object™ from the Audio Toolbox™ to implement a multiband dynamic range compressor model. You can run the model on your host computer or deploy it to an Apple iOS device.

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Generate Standalone Executable for Parametric Audio Equalizer

Generate a standalone executable for parametric equalization using MATLAB Coder™ and use it on an audio file.multibandParametricEQis used for the equalization algorithm. The example allows you to dynamically adjust the coefficients of the filters using a user interface (UI) that is running in MATLAB.

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Deploy Audio Applications with MATLAB Compiler

Use MATLAB Compiler™ to create a standalone application from a MATLAB function. The function implements an audio processing algorithm and plays the result through your audio output device.

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Desktop Real-Time Audio Acceleration with MATLAB Coder

Accelerate a real-time audio application using C code generation withMATLAB Coder.

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