Binaural hearing for robots

Course description

This archived course remains open to registrations although it is not facilitated by the course teachers : its contents are no longer updated and may therefore no longer be up to date. The last version of this Mooc Binaural Hearing for Robots dates back to May 2015. If you register, you can freely consult the read-only resources but all collaborative spaces are closed (forums, wiki and other collaborative exercises) : you cannot interact with the teaching team or with other learners. Furthermore, you will not be able to obtain an attestation of achievement for this course.

Robots have gradually moved from factory floors to populated areas. Therefore, there is a crucial need to endow robots with perceptual and interaction skills enabling them to communicate with people in the most natural way. With auditory signals distinctively characterizing physical environments and speech being the most effective means of communication among people, robots must be able to fully extract the rich auditory information from their environment.

This course will address fundamental issues in robot hearing; it will describe methodologies requiring two or more microphones embedded into a robot head, thus enabling sound-source localization, sound-source separation, and fusion of auditory and visual information.

The course will start by briefly describing the role of hearing in human-robot interaction, overviewing the human binaural system, and introducing the computational auditory scene analysis paradigm. Then, it will describe in detail sound propagation models, audio signal processing techniques, geometric models for source localization, and unsupervised and supervised machine learning techniques for characterizing binaural hearing, fusing acoustic and visual data, and designing practical algorithms. The course will be illustrated with numerous videos shot in the author’s laboratory.

Who is this course for?

The course is intended for Master of Science students with good background in signal processing and machine learning. The course is also valuable to PhD students, researchers and practitioners, who work in signal and image processing, machine learning, robotics, or human-machine interaction, and who wish to acquire competences in binaural hearing methodologies.

Course outline

• Module 1: Introduction to Robot Hearing
• Module 2 : Methodological Foundations
• Module 3 : Sound-Source Localization
• Module 4 : Machine Learning and Binaural Hearing
• Module 5 : Fusion of Audio and Vision

Pedagogical team

Authors:

• Radu Patrice Horaud, research director, Inria

Pedagogical support:

• Laurence Farhi, Learning engineer, Inria Learning Lab