  Automatic estimation of domestic activities from audio can be used to solve
many problems, such as reducing the labor cost for nursing the elderly people.
This study focuses on solving the problem of domestic activity clustering from
audio. The target of domestic activity clustering is to cluster audio clips
which belong to the same category of domestic activity into one cluster in an
unsupervised way. In this paper, we propose a method of domestic activity
clustering using a depthwise separable convolutional autoencoder network. In
the proposed method, initial embeddings are learned by the depthwise separable
convolutional autoencoder, and a clustering-oriented loss is designed to
jointly optimize embedding refinement and cluster assignment. Different methods
are evaluated on a public dataset (a derivative of the SINS dataset) used in
the challenge on Detection and Classification of Acoustic Scenes and Events
(DCASE) in 2018. Our method obtains the normalized mutual information (NMI)
score of 54.46%, and the clustering accuracy (CA) score of 63.64%, and
outperforms state-of-the-art methods in terms of NMI and CA. In addition, both
computational complexity and memory requirement of our method is lower than
that of previous deep-model-based methods. Codes:
https://github.com/vinceasvp/domestic-activity-clustering-from-audio
