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 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.
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.
No attestation of achievement will be delivered for this course.
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