Skip to main content
FUN MOOC
  • FAQ
  • Home
  • News
  • Courses
  • GRADEO
  • Diplômes
  • Organizations
  • You are here:
  • Home
  • Courses
  • Binaural Hearing for Robots

Binaural Hearing for Robots

Ref. 41004
CategoryComputer science and programmingCategoryDigital and technology
  • Duration: 5 weeks
  • Effort: 10 hours
  • Pace: ~2 hours/week
Enrollment
From Feb. 11, 2014 to ...
Course
From May 11, 2015 to June 12, 2015
Languages
English

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archived course remains open to registrations although it is not facilitated by the course teachers : its contents are no longer updated and may therefore no longer be up to date.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.

Format

The course contents are structured around 5 weeks, however all the contents will be available from the opening of the MOOC. Each week consists in approximately 10 sessions : each one containing a video about 6 minutes and quizzes.

This session is an archived Mooc permanently open.

Prerequisites

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

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.

Assessment and certification

No attestation of achievement will be delivered for this course.

Course plan

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

Other course runs

Archived

  • From May 11, 2015 to June 13, 2015

Course team

Radu Patrice Horaud

Categories

Radu Patrice Horaud holds a position of research director at INRIA Grenoble Rhône-Alpes.

Organizations

Inria

License

License for the course content

Attribution-NonCommercial-NoDerivatives

You are free to:

  • Share — copy and redistribute the material in any medium or format

Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.

License for the content created by course participants

Attribution-NonCommercial-NoDerivatives

You are free to:

  • Share — copy and redistribute the material in any medium or format

Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
FacebookTwitterLinkedin

Learn more

  • Help and contact
  • About FUN
  • Legal
  • Privacy policy
  • User's charter
  • General Terms and Conditions of Use
  • Sitemap
  • Cookie management
Powered by Richie