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Binaural Hearing for Robots

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 can attend this course ?
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.
The course material will allow the attendants to design and develop robot and machine hearing algorithms.

Recommended Background
Introductory courses in digital signal processing, probability and statistics, computer science.

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

The material of this course come from a MOOC delivered on France Université Numérique :


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