code generation and deployment -凯发k8网页登录

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

after you develop your application using audio toolbox™ features, you can generate portable c/c source code, standalone executables, or standalone applications from your matlab^® code or simulink^® model. with code generation, you can:

run your algorithm on machines that do not have matlab or simulink installed.
speed up processing while you work in the matlab or simulink environment.
embed your algorithm in hardware.
incorporate your algorithms into legacy code.

this table compares deployment characteristics of matlab compiler™ and matlab coder™ functionality. use this table to decide how to deploy your matlab code or simulink model.

deployment characteristic	using matlab compiler functionality	using matlab coder functionality
output	nonreadable shared libraries.	portable and readable c source code.
library dependency	.	library dependency only for audio i/o features: .
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, see .

to deploy an audio plugin to simulink, see .

topics

introduces code generation support.
set your system environment to use dynamic library files for deployed audio i/o system objects and blocks.
(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

(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.

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.