ROS open-source audio recognizer: ROAR environmental sound detection tools for robot programming

Advances in audio recognition have enabled the real-world success of a wide variety of interactive voice systems over the last two decades. More recently, these same techniques have shown promise in recognizing non-speech audio events. Sounds are ubiquitous in real-world manipulation, such as the cl...

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Bibliographic Details
Published in:Autonomous robots 2013-04, Vol.34 (3), p.207-215
Main Authors: Romano, Joseph M., Brindza, Jordan P., Kuchenbecker, Katherine J.
Format: Article
Language:English
Subjects:
Acoustics
Artificial Intelligence
Audio data
Autonomous
Background noise
Computer Imaging
Computer programs
Control
Engineering
Feature extraction
Freeware
Hand tools
Interactive systems
Mechatronics
Open source software
Pattern Recognition and Graphics
Programming
Recognition
Robotics
Robotics and Automation
Robots
Sound
Source code
Speech recognition
Supervised learning
Support vector machines
Vision
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Summary:Advances in audio recognition have enabled the real-world success of a wide variety of interactive voice systems over the last two decades. More recently, these same techniques have shown promise in recognizing non-speech audio events. Sounds are ubiquitous in real-world manipulation, such as the click of a button, the crash of an object being knocked over, and the whine of activation from an electric power tool. Surprisingly, very few autonomous robots leverage audio feedback to improve their performance. Modern audio recognition techniques exist that are capable of learning and recognizing real-world sounds, but few implementations exist that are easily incorporated into modern robotic programming frameworks. This paper presents a new software library known as the ROS Open-source Audio Recognizer (ROAR). ROAR provides a complete set of end-to-end tools for online supervised learning of new audio events, feature extraction, automatic one-class Support Vector Machine model tuning, and real-time audio event detection. Through implementation on a Barrett WAM arm, we show that combining the contextual information of the manipulation action with a set of learned audio events yields significant improvements in robotic task-completion rates.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-013-9323-6