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 BackgroundIntroductory courses in digital signal processing, probability and statistics, computer science.

Course SyllabusPart 1: Introduction to Robot HearingPart 2 : Methodological FoundationsPart 3 : Sound-Source LocalizationPart 4 : Machine Learning and Binaural HearingPart 5 : Fusion of Audio and Vision

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


Vidéo pédagogique

Audio-visual processing challenges
9 vidéos
5 : Fusion of Audio and Vision
  • HORAUD Radu
The fifth and last part of the video lectures will address the fusion of auditory and visual data. We will start with the motivation behind audio-visual fusion, followed by a short overview of the visual features that are likely to be used. We will describe audio-visual fusion in the temporal and in the spectral domains and we will present a few examples using the audio-visual head of the NAO robot. This part will complete the course that described the main methodologies needed to perform hearing with a binaural robot head.
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Time difference of arrival (TDOA)
10 vidéos
3 : Sound-Source Localization
  • HORAUD Radu
During the third part, we will study Sound-Source Localization: time difference of arrival (TDOA), estimation of TDOA by cross-correlation, in the temporal and spectral domains, the geometry of multiple microphones, embedding the microphones in a robot head, predicting direction of a sound with a robot head and an example of sound direction estimation.
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