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Seminar “Machine Learning for Social Signal Processing” SS22

Seminar Title: “Machine Learning for Social Signal Processing SS2022”

News

  • 22.04.2022 – Projects description available for download (encrypted, check your emails)
  • 19.04.2022 – Day 1 slides available for download (encrypted, check your emails)
  • 05.04.2022 – The first meeting will be on Tuesday 19th April @ 14:15 !!!

Meeting

The meeting will be on the Teams platform: https://teams.microsoft.com/l/meetup-join/19%3ameeting_OGQ0MTdjNTMtMjMyNS00YTVkLWFmNjgtMmQzMGY2MGU0N2Ix%40thread.v2/0?context=%7b%22Tid%22%3a%2261a9f1bd-7ea0-4068-b231-bb4a6bfcb700%22%2c%22Oid%22%3a%22f49bb6ca-63c9-41d2-aca7-7957156b0fa5%22%7d

Description

Social signals like eye contact, prosody, facial expressions, and body language are central to human social life. In this seminar, we will explore how machine learning can be used to detect and interpret such signals.

Participants will form small groups and can choose from a number of possible projects, including:

  • Eye contact detection;
  • Inferring leadership, rapport, or liking from social signals in group interactions;
  • Detecting backchanneling behaviour;
  • Detecting medical conditions from speech behaviour.

To keep projects inside a reasonable scope for a seminar and to allow students to focus on the development of machine learning approaches, we will provide well-documented datasets, annotations, as well as relevant pre-computed input features.

Number of Participants: 6-12 students

Requirements

  • The seminar is targeted at master students interested in pursuing research in the social signal processing field;
  • Knowledge in Machine Learning (e.g. Machine Learning core lecture) and preferably also speech processing and computer vision;
  • Practical experience with scientific computing in Python.

Tutors: Philipp Müller, Hali Lindsay, Chirag Bhuvaneshwara, Fabrizio Nunnari.
This seminar is held in cooperation with the Ambient Assisted Living group at DFKI.

Seminar Website: https://affective.dfki.de/teaching-2/machine-learning-for-social-signal-processing-ss2022/

LSF page: https://www.lsf.uni-saarland.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=137291&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung

Material

Slides for Students – Day 1 – pwd (19.04.2022)

Projects description