ODAS: Open embeddeD Audition System
This work addresses the challenge of implementing artificial audition on robots with limited embedded computing capabilities, though it appears incremental as it builds on existing frameworks.
The paper tackles the problem of high computational load in existing robot audition frameworks by introducing ODAS, an open embedded audition system that reduces computational requirements, enabling sound source localization, tracking, and separation on low-cost embedded systems.
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, although 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.