Audio-based group detection for classroom dynamics analysis

Yudong Tao, Samantha G. Mitsven, Lynn K. Perry, Daniel S. Messinger, Mei Ling Shyu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Group detection is a fundamental problem in sociological and behavioral data analysis and has attracted much attention in recent years. Most of the current approaches focus on using visual data, such as still images and videos, to detect groups. One of the most important applications of group detection is to assist psychologists to understand the classroom dynamics. However, the camera recordings may be unavailable and it could be infeasible to set up the cameras without blind spots. Therefore, as an alternative approach to group detection, we propose an audio-based framework that utilizes multiple synchronized audio data streams collected from wearable devices on each subject. In this paper, the audio recordings collected from a preschool classroom over multiple days are used to produce the group detection results which are validated by clustering the subject locations collected along with the audio data. The experiment shows on average 0.391 Normalized Mutual Information (NMI) scores for the detected groups by the audio-based framework and location-based clustering.

Original languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
EditorsPanagiotis Papapetrou, Xueqi Cheng, Qing He
PublisherIEEE Computer Society
Number of pages8
ISBN (Electronic)9781728146034
StatePublished - Nov 2019
Event19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 - Beijing, China
Duration: Nov 8 2019Nov 11 2019

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259


Conference19th IEEE International Conference on Data Mining Workshops, ICDMW 2019


  • Audio processing
  • Classroom dynamics analysis
  • Group detection

ASJC Scopus subject areas

  • Computer Science Applications
  • Software


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