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Audio Localization for Robots Using Parallel Cerebellar Models

Baxendale, Mark; Pearson, Martin; Nibouche, Mokhtar; Secco, Emanuele; Pipe, Tony


Mark Baxendale

Emanuele Secco


© 2016 IEEE. A robot audio localization system is presented that combines the outputs of multiple adaptive filter models of the Cerebellum to calibrate a robot's audio map for various acoustic environments. The system is inspired by the MOdular Selection for Identification and Control (MOSAIC) framework. This study extends our previous work that used multiple cerebellar models to determine the acoustic environment in which a robot is operating. Here, the system selects a set of models and combines their outputs in proportion to the likelihood that each is responsible for calibrating the audio map as a robot moves between different acoustic environments or contexts. The system was able to select an appropriate set of models, achieving a performance better than that of a single model trained in all contexts, including novel contexts, as well as a baseline generalized cross correlation with phase transform sound source localization algorithm. The main contribution of this letter is the combination of multiple calibrators to allow a robot operating in the field to adapt to a range of different acoustic environments. The best performances were observed where the presence of a Responsibility Predictor was simulated.


Baxendale, M., Pearson, M., Nibouche, M., Secco, E., & Pipe, T. (2018). Audio Localization for Robots Using Parallel Cerebellar Models. IEEE Robotics and Automation Letters, 3(4), 3185-3192.

Journal Article Type Article
Acceptance Date Jun 21, 2018
Publication Date Oct 1, 2018
Deposit Date Dec 13, 2018
Journal IEEE Robotics and Automation Letters
Print ISSN 2377-3766
Electronic ISSN 2377-3766
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 3
Issue 4
Pages 3185-3192
Keywords localization, learning and adaptive systems, robot audition
Public URL
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Additional Information Additional Information : (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.


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