Artificial audition aims at providing hearing capabilities to machines, computers and robots. Existing frameworks in robot audition offer interesting sound source localization, tracking and separation performance, but involve a significant amount of computations that limit their use on robots with embedded computing capabilities. This paper presents ODAS, the Open embeddeD Audition System framework, which includes strategies to reduce the computational load and perform robot audition tasks on low-cost embedded computing systems. It presents key features of ODAS, along with cases illustrating its uses in different robots and artificial audition applications.

翻译：人工试镜旨在为机器、计算机和机器人提供听力能力。机器人试镜的现有框架提供了有趣的可靠源本地化、跟踪和分离性能,但涉及大量计算,限制了它们用于内嵌计算能力的机器人。 本文介绍了Open InspenddeD审计系统框架,其中包括减少计算负荷和在低成本嵌入式计算机系统上执行机器人试镜任务的战略。 它展示了ODAS的关键特征,以及显示其在不同的机器人和人工试镜应用中的用途的案例。