During a few of their most early life, many children go to day care centers outside their homes. While there, they require a supportive, healthy environment that features meaningful speech and conversation. This hinges on the soundscape of the kid care center.

In his presentation on the 183rd Meeting of the Acoustical Society of America, Kenton Hummel of the University of Nebraska–Lincoln will describe how soundscape research in day cares can improve child and provider outcomes and experiences. The presentation, “Applying unsupervised machine learning clustering techniques to early childcare soundscapes,” will happen Dec. 8 at 11:25 a.m. Eastern U.S. within the Summit A room, as a part of the meeting running Dec. 5-9 on the Grand Hyatt Nashville Hotel.

Few studies have rigorously examined the indoor sound quality of kid care centers. The scarcity of research may deprive providers and engineers from providing the best quality of care possible. This study goals to higher understand the sound environment of kid care centers to pave the best way toward higher child care.”

Kenton Hummel, University of Nebraska–Lincoln

The goal of the research is to grasp the connection between noise and folks. High noise levels and long periods of loud fluctuating sound can negatively impact children and staff by increasing the hassle it takes to speak. In contrast, a low background noise level allows for meaningful speech, which is important for language, brain, cognitive, and social/emotional development.

Hummel is a member of the UNL Soundscape Lab led by Erica Ryherd. Their team collaborated with experts in engineering, sensing, early child care, and health to watch three day care centers for 48-hour periods. In addition they asked staff to judge the sound of their workplace. From there, they used machine learning to characterize the acoustic environment and determine what aspects influence the kid and provider experience.

“Recent work in offices, hospitals, and schools has utilized machine learning to grasp their respective environments in a way that goes beyond typical acoustic analyses,” said Hummel. “This work utilizes similar machine learning techniques to construct and expand on that work.”

Source:

Acoustical Society of America