Audio Localization for Robots Using Parallel Cerebellar Models

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

Abstract

© 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.

Citation

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. https://doi.org/10.1109/LRA.2018.2850447

• Journal Article Type: Article
• Acceptance Date: Jun 21, 2018
• Publication Date: Oct 1, 2018
• Deposit Date: Dec 13, 2018
• Publicly Available 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
• DOI: https://doi.org/10.1109/LRA.2018.2850447
• Keywords: localization, learning and adaptive systems, robot audition
• Public URL: https://uwe-repository.worktribe.com/output/859637
• Publisher URL: http://dx.doi.org/10.1109/LRA.2018.2850447
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