Elsevier

Robotics and Autonomous Systems

Volume 96, October 2017, Pages 184-210
Robotics and Autonomous Systems

Localization of sound sources in robotics: A review

https://doi.org/10.1016/j.robot.2017.07.011Get rights and content
Under a Creative Commons license
open access

Highlights

  • A highly detailed survey of sound source localization (SSL) used over robotic platforms.

  • Classification of SSL techniques and description of the SSL problem.

  • Description of the diverse facets of the SSL problem.

  • Survey of the evaluation methodologies used to measure SSL performance in robotics.

  • Discussion of current SSL challenges and research questions.

Abstract

Sound source localization (SSL) in a robotic platform has been essential in the overall scheme of robot audition. It allows a robot to locate a sound source by sound alone. It has an important impact on other robot audition modules, such as source separation, and it enriches human–robot interaction by complementing the robot’s perceptual capabilities. The main objective of this review is to thoroughly map the current state of the SSL field for the reader and provide a starting point to SSL in robotics. To this effect, we present: the evolution and historical context of SSL in robotics; an extensive review and classification of SSL techniques and popular tracking methodologies; different facets of SSL as well as its state-of-the-art; evaluation methodologies used for SSL; and a set of challenges and research motivations.

Keywords

Robot audition
Sound source localization
Direction-of-arrival
Distance estimation
Tracking

Cited by (0)

Caleb Rascon is a researcher in the Instituto de Investigaciones en Matematicas Aplicadas y en Sistemas (IIMAS) of the Universidad Nacional Autónoma de México (UNAM) and member of the service robotics group Golem. He received his bachelor degree in Electronic Systems Engineering in ITESM, and his Ph.D. in the University of Manchester. He was awarded as an Innovator under 35 in the Mexico 2014 edition of the MIT Technology Review, and has authored several papers and conducted keynotes on the topic of Robot Audition. His other research interest are Control Engineering, Machine Learning, and Service Robots.

Ivan Meza is a research assistant at IIMAS, UNAM working at the Department of Computer Science on the Golem group. He received his bachelor degree in Computer Engineering in UNAM, and his Ph.D. in the School of Informatics of the University of Edinburgh. He has authored several papers on the topics of Human–Robot Interaction, Computational Linguistics, and Machine Learning, as well organized summer internships and conference workshops on the matter. His other research interest are Deep learning, Natural Language Processing, Dialogue Systems and Service Robots.