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Social Signal Processing


Admission requirements

Not applicable.


The core of social intelligence is our ability to understand and interpret social signals of a person we are communicating with. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. Social Signal Processing (SSP) is the new, emerging domain aimed at understanding social interactions through machine analysis and production of nonverbal behavior. In this course you will learn how next-generation computing can make use of such social signals by giving it the ability to recognize and produce human social signals and social behaviors. Think about turn taking, politeness, disagreement, emotions, rapport. You will learn about relevant findings in social psychology, and you will learn computational techniques that allow systems to make use of social signals to become more effective and more efficient by being able to detect but also simulate (e.g. in virtual agents) blinks, smiles, crossed arms, laughter. Socially aware computing. These techniques can be used in robots, virtual agents, smart homes, crowd monitoring, etc.

Course objectives

  • Position the field of social signal processing in computer science and psychology, and identify its major goals and angles of study.

  • Define and explain social signals in humans and know about major psychological theories of social interaction.

  • Explain major social signal analysis and synthesis techniques.

  • Perform a small research project in groups that studies social signals processing, involving software and or hardware development, a user study or other form of evaluation and a final paper.


The most recent timetable can be found on the students' website.

Mode of instruction

  • Seminar

  • Self-study

  • Project work

Course load

Total hours of study: 168 hrs. (= 6 EC)
Lectures: 6:00 hrs.
Practical work 80:00 hrs.
Examination: 2:00 hrs.
Other (self-study and participation in meetings): 80:00 hrs.

Assessment method

  • Project group work (50%): research project and paper and presentation.

  • Project proposal (25%): the proposal for the project and the defence of it during a presentation.

  • Theory (25%): MC exam after three weeks of theory. Passing is compulsory.


See Blackboard.

Reading list

  • Selected papers made available before the course.


  • You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.

  • Please also register for the course in Blackboard.


Lecturer: D.J. Broekens