Audio Localization for Robots using Parallel Cerebellar Models

Baxendale, Mark and Pearson, Martin and Nibouche, Mokhtar and Secco, Emanuele Lindo and Pipe, Tony (2018) Audio Localization for Robots using Parallel Cerebellar Models. IEEE Robotics and Automation Letters. ISSN 2377-3766

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Official URL: https://ieeexplore.ieee.org/document/8395347/

Abstract

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 GCC-PHAT sound source localization algorithm. The main contribution of this work 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.

Item Type: Article
Additional Information and Comments: (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.
Keywords: Localization, Learning and Adaptive Systems, Robot Audition.
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Mark Baxendale
Date Deposited: 04 Jul 2018 08:43
Last Modified: 04 Jul 2018 08:43
URI: http://hira.hope.ac.uk/id/eprint/2559

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