# ODAS: Open embeddeD Audition System

arXiv: https://arxiv.org/abs/2103.03954 · HTML: https://mlsanity.com/paper/2103.03954/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2021.

## Built on by (most cited first, 5 of 5)

- [Direction-Aware Adaptive Online Neural Speech Enhancement with an Augmented Reality Headset in Real Noisy Conversational Environments](https://mlsanity.com/paper/2207.07296/) (2022)
- [Multi-agent Auditory Scene Analysis](https://mlsanity.com/paper/2507.02755/) (2025)
- [Articulated Humanoid Head for a Robot Receptionist Capable of Natural Human Interaction](https://mlsanity.com/paper/2607.17042/) (2026)
- [AcousticDiffusion: Semantically Conditioned Audio-Guided Diffusion Policy for Search-and-Rescue Assistance](https://mlsanity.com/paper/2609.21792/) (2026)
- [Multi-agent Auditory Scene Analysis: Improved Localization Speed and Robustness by Multi-beamformed Speech Quality Feedback](https://mlsanity.com/paper/2610.00538/) (2026)

## Abstract (arXiv)

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.