  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.
