ICASSP 2021accepted0 citations

Detecting Acoustic Reflectors Using A Robot's Ego-Noise

Usama Saqib, Antoine Deleforge, Jesper Rindom Jensen

Abstract

In this paper, we propose a method to estimate the proximity of an acoustic reflector, e.g., a wall, using ego-noise, i.e., the noise produced by the moving parts of a listening robot. This is achieved by estimating the times of arrival of acoustic echoes reflected from the surface. Simulated experiments show that the proposed non-intrusive approach is capable of accurately estimating the distance of a reflector up to 1 meter and outperforms a previously proposed intrusive approach under loud ego-noise conditions. The proposed method is helped by a probabilistic echo detector that estimates whether or not an acoustic reflector is within a short range of the robotic platform. This preliminary investigation paves the way towards a new kind of collision avoidance system that would purely rely on audio sensors rather than conventional proximity sensors.

BibTeX
@inproceedings{icassp2021_detectingacousti,
  title = {Detecting Acoustic Reflectors Using A Robot's Ego-Noise},
  author = {Usama Saqib and Antoine Deleforge and Jesper Rindom Jensen},
  booktitle = {ICASSP 2021},
  year = {2021}
}
Detecting Acoustic Reflectors Using A Robot's Ego-Noise · ICASSP 2021