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JACIII Vol.22 No.2 pp. 242-248
doi: 10.20965/jaciii.2018.p0242
(2018)

Paper:

Estimating Classroom Situations by Using CNN with Environmental Sound Spectrograms

Misaki Kitahashi and Hisashi Handa

Kindai Universsity
3-4-1 Kowakae, Higashi-Osaka 577-8502, Japan

Received:
August 27, 2017
Accepted:
January 11, 2018
Published:
March 20, 2018
Keywords:
classroom situations, learning analytics, convolutional neural networks
Abstract

The Learning Analytics is a research area that seeks to understand learning processes by using the various computer science techniques. In this paper, we focus on the analysis of certain classroom situations, such as lecturings, performing exercises, and testing. These analyses do not directory apply to students; however, they are very useful for analyzing and interpreting students’ behaviors in the classroom. This is significant as students’ behaviors can affect very real changes in classroom situations. This paper employs the Convolutional Neural Networks to identify various classroom situations from the spectrograms of environmental sounds in the classroom. Experimental results show the effectiveness of the proposed systems.

Cite this article as:
M. Kitahashi and H. Handa, “Estimating Classroom Situations by Using CNN with Environmental Sound Spectrograms,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.2, pp. 242-248, 2018.
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References
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Last updated on Sep. 21, 2018