Journal
APPLIED ACOUSTICS
Volume 169, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apacoust.2020.107448
Keywords
Student activity; Classroom acoustics; Machine learning; Gaussian Mixture Model; K-means clustering
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The background noise has a fundamental role in oral communication, since the higher the speech level with respect to the background noise the greater the intelligibility. In occupied lecture halls the main contribution to background noise is related to the human noise, which is called by scholars student activity. Scholars proposed methods to measure both student activity and speech level through short-time sound level meter measurements during lessons. However, a comparison of their relative effectiveness on a relevant set of data in different situations is still lacking. In this study, basing on recordings of university lessons performed with public address system, student activity and speech level values were extracted using different methods. Various scenarios of university lectures were recorded: frontal lessons, media-aided lectures, open discussions. Visual-segmentation and blind-segmentation procedures were compared for each case. Results show the benefits of blind-segmentation methods, which seem to be reliable and affordable methods for this kind of analyses. (C) 2020 Elsevier Ltd. All rights reserved.
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