ODAS: Open embeddeD Audition System

Abstract

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. I. INTRODUCTIONSimilarly to artificial/computer vision, artificial/computer audition can be defined as the ability to provide hearing capabilities to machines, computers and robots. Vocal assistants on smart phones and smart speakers are now common, providing a vocal interface between people and devices [1]. But as for artificial vision, there are still many problems to resolve for endowing robots with adequate hearing capabilities, such as ego and non-stationary noise cancellation, mobile and distant speech and sound understanding [2]- [6].Open source software frameworks, such as OpenCV [7] for vision and ROS [8] for robotics, greatly contribute in making these research fields evolve and progress, allowing the research community to share and mutually benefit from collective efforts. In artificial audition, two main frameworks exist: • HARK (Honda Research Institute Japan Audition for Robots with Kyoto University 1 ) provides multiple modules for sound source localization and separation [9]-[11]. This framework is mostly built over the FlowDesigner software [12], and can also be interfaced with speech recognition tools such as Julius [13] and Kaldi [14], [15]. HARK implements sound source localization in 2-D using variants of the Multiple Signal Classification (MUSIC) algorithm [16]-[18]. HARK also performs geometrically-constrained higher-order decorrelation-based source separation with adaptive *This work was supported by FRQNT -Fonds recherche Québec Nature et Technologie.

Fast Cross-Correlation for TDoA Estimation on Small Aperture Microphone Arrays

“…It demonstrates that the amount of flops between FCC and GCC is reduced by a factor of 4.5, and the The next step consists in using FCC to estimate TDoAs for each pair of microphones in a microphone array, and to perform a search for the best DoA candidate. This functionality could be added to existing frameworks such as ODAS [27], and exploits optimization strategies introduced with SMP-PHAT for microphone arrays that show some symmetry [36]. We also plan to investigate multiple TDoAs estimation, using a similar approach to what is currently done with SVD-PHAT [64].…”

“…SSL can also serve numerous applications in robotics [19], ranging from acoustic synchronous localization and mapping (SLAM) [20], rescue missions [21], [22], drones tracking [23], [24], [25] and assisting humans with hearing impairments [26]. Some frameworks have been proposed over the years to perform online SSL on robots [27], [28].…”