Care-O-bot II—Development of a Next Generation Robotic Home Assistant
Autonomous Robots
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction
Robotics and Autonomous Systems
Computational Auditory Scene Analysis and Its Application to Robot Audition: Five Years Experience
ICKS '07 Proceedings of the Second International Conference on Informatics Research for Development of Knowledge Society Infrastructure
Springer Handbook of Speech Processing
Springer Handbook of Speech Processing
Improved one-class SVM classifier for sounds classification
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Surveillance Robot Utilizing Video and Audio Information
Journal of Intelligent and Robotic Systems
Non-speech audio event detection
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
HERB: a home exploring robotic butler
Autonomous Robots
Rollin' Justin: mobile platform with variable base
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
The SHOGUN Machine Learning Toolbox
The Journal of Machine Learning Research
A flexible framework for key audio effects detection and auditory context inference
IEEE Transactions on Audio, Speech, and Language Processing
Robust Recognition of Simultaneous Speech by a Mobile Robot
IEEE Transactions on Robotics
Human-Inspired Robotic Grasp Control With Tactile Sensing
IEEE Transactions on Robotics
Hi-index | 0.00 |
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.