Applying unsupervised machine learning clustering techniques to early childcare soundscapes

Authors

Kenton Hummel, University of Nebraska-LincolnFollow
Erica E. Ryherd, University of Nebraska-LincolnFollow
Iason Konstantzos, University of Nebraska - LincolnFollow
Abbie Raikes, University of Nebraska - LincolnFollow

Document Type

Article

Date of this Version

12-9-2022

Citation

Proceedings of Meetings on Acoustics, Vol. 50, 015002 (2023). https://doi.org/10.1121/2.0001708

Comments

Used by permission.

Abstract

Early childhood is a critical time period for language, brain, cognitive, and social/emotional development. Out-of-home childcare is a normative, typical experience for millions of young children. Although Indoor Environmental Quality (IEQ) in K-12 settings has received recent, significant attention, the links between IEQ and children’s learning and development in early childcare settings is a less understood topic. This work focuses specifically on the sound aspect of IEQ in early childcare settings to better understand typical noise levels and occupant experience. Standard approaches to analyzing background noise will be presented alongside more detailed statistical analyses utilizing unsupervised machine learning clustering techniques. Noise data collected in three daycares will be presented using typical acoustic metrics and clustering techniques to better understand room activity conditions and support new metrics. Overall, this study can lead to a better understanding of daycare soundscapes and pave the way towards a better childcare for young children.