2019/12/10 by Priyanka Khante, Mai Lee Chang, Khante, Priyanka +8
Computer Science · Engineering · Health Professions · #Artificial intelligence #Audio and Speech Processing (eess.AS) #CHAOS (operating system) #Computer science #Environmental science #FOS: Computer and information sciences #FOS: Electrical engineering #Infant Health and Development #Machine Learning (cs.LG) #Music and Audio Processing #Speech and Audio Processing #Unsupervised learning #cs.LG #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1912.04844
published in arXiv (Cornell University) (Cornell University)
arxiv created 2019/12/10 · openalex publication_date 2019/12/10 · arxiv updated 2019/12/11 · openalex created_date 2019/12/26 · openalex updated_date 2026/07/28
Acoustic environments vary dramatically within the home setting. They can be a source of comfort and tranquility or chaos that can lead to less optimal cognitive development in children. Research to date has only subjectively measured household chaos. In this work, we use three unsupervised machine learning techniques to quantify household chaos in infants' homes. These unsupervised techniques include hierarchical clustering using K-Means, clustering using self-organizing map (SOM) and deep learning. We evaluated these techniques using data from 9 participants which is a total of 197 hours. Results show that these techniques are promising to quantify household chaos.