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Towards an enactive robot audition architecture

Abstract : Robots are usually equipped with advanced capabilities in order to autonomously adapt to real and dynamic environments and to interact with humans. Robot Perception is being inspired by new embodied cognition approaches that redefine the notions of perception, cognition and action, basic processes of intelligent behaviour. Enactive approaches consider the perceptual act as a consequence in action of the structural coupling between the organism and its environment in seek of significance. There is a growing interest in the development of robot perception systems based on new architectures to materialize naturally these action-perception functions. In this direction, we propose an evolution of the EAR sensor [4], which fulfills the constraints of mobile robot audition, such as embeddability, synchronous multichannel acquisition and real-time execution. Using Systems-on-a-Programmable-Chip (SoPCs) methodology, this sensor incorporates a novel architecture that offers all the basic calculation blocks necessary to perform most binaural and array auditory functions, and allows to easily develop new functionalities and connections between motor and perceptual modules in order to implement enactive behaviour. Moreover, Microelectromechanical Systems (MEMS) microphones have been studied and implemented, enabling the acquisition of high-fidelity audio on inexpensive and portable devices. In this paper , performance results are presented for sound source detection and localization functions, and progress is shown towards the implementation of MEMS microphones in Human-Machine Interfaces and Robot Audition. Finally, evolutions towards an interdisciplinary design of enactive audition functions are discussed.
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https://hal.laas.fr/hal-01969314
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  • HAL Id : hal-01969314, version 1

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Valentín Lunati, Patrick Danès, Claudia Arias, Fernando Bermejo, Fernando González, et al.. Towards an enactive robot audition architecture. International Congress on Acoustics (ICA 2016), Sep 2016, Buenos Aires, Argentina. ⟨hal-01969314⟩

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