Book Image

Rapid BeagleBoard Prototyping with MATLAB and Simulink

Book Image

Rapid BeagleBoard Prototyping with MATLAB and Simulink

Overview of this book

As an open source embedded single-board computer with many standard interfaces, Beagleboard is ideal for building embedded audio/video systems to realize your practical ideas. The challenge is how to design and implement a good digital processing algorithm on Beagleboard quickly and easily without intensive low-level coding. Rapid BeagleBoard Prototyping with MATLAB and Simulink is a practical, hands-on guide providing you with a number of clear, step-by-step exercises which will help you take advantage of the power of Beagleboard and give you a good grounding in rapid prototyping techniques for your audio/video applications. Rapid BeagleBoard Prototyping with MATLAB and Simulink looks at rapid prototyping and how to apply these techniques to your audio/video applications with Beagleboard quickly and painlessly without intensive manual low-level coding. It will take you through a number of clear, practical recipes that will help you to take advantage of both the Beagleboard hardware platform and Matlab/Simulink signal processing. We will also take a look at building S-function blocks that work as hardware drivers and interfaces for Matlab/Simulink. This gives you more freedom to explore the full range of advantages provided by Beagleboard. By the end of this book, you will have a clear idea about Beagleboard and Matlab/Simulink rapid prototyping as well as how to develop voice recognition systems, motion detection systems with I/O access, and serial communication for your own applications such as a smart home.
Table of Contents (15 chapters)
Rapid BeagleBoard Prototyping with MATLAB and Simulink
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Recognition session


The core task in a recognition session is related to the so-called pattern matching. In our case, patterns are the codebooks derived from the training voices and each pattern is linked to a class with a specific meaning, ON or OFF. The goal of pattern matching is to classify the new input voice of interest into one of these two classes. Our recognition session is to match the MFCC features of the new input voice to one of the existing codebooks.

The voice recognition is implemented in a Simulink model VocRcgBB_Rcg.mdl, as shown in the following screenshot:

Similar to the Simulink model for training, the voice recognition model has the same pre-processing blocks, including data normalization, voice detection, and segmentation. The difference is that the Training subsystem is replaced by the Recognition subsystem and a new GPIO Write block is used to control the user LED.

The Recognition subsystem takes three inputs:

  • 1 second buffered audio data

  • codebkOFF for voice command...