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Self-adaptive context aware audio localization

Baxendale, Mark; Pearson, Martin J; Nibouche, Mokhtar; Secco, M; Pipe, A G


Mark Baxendale

M Secco


Gao Yang

Saber Fallah

Yaochu Jin

Constantina Lekakou


An audio sensor system is presented that uses multiple cere-
bellar models to determine the acoustic environment in which a robot is operating, allowing the robot to select appropriate models to calibrate its audio-motor map for the detected environment. There are two key areas of novelty here. One is the application of cerebellar models in a new context, that is auditory sensory input. The second is the idea of applying a multiple models approach to motor control to a sensory prob-
lem rather than a motor problem. The use of the adaptive filter model of the cerebellum in a variety of robotics applications has demonstrated the utility of the so-called cerebellar chip. This paper combines the notion of cerebellar calibration of a distorted audio-motor map with the use of multiple parallel models to predict the context (acoustic environment) within which the robot is operating. The system was able to correctly
predict seven different acoustic contexts in almost 70% of cases tested.


Baxendale, M., Pearson, M. J., Nibouche, M., Secco, M., & Pipe, A. G. (2017). Self-adaptive context aware audio localization. In C. Lekakou, Y. Jin, S. Fallah, & G. Yang (Eds.), Towards Autonomous Robotic Systems, 66-78. Springer link

Conference Name Towards Autonomous robotic systems 2017
Acceptance Date Jul 20, 2017
Publication Date Jan 1, 2017
Peer Reviewed Peer Reviewed
Pages 66-78
Book Title Towards Autonomous Robotic Systems
ISBN 9783319641072
Keywords audio map calibration, cerebellar inspired
Publisher URL
Additional Information Title of Conference or Conference Proceedings : Towards Autonomous Robotic Systems 2017