  Recent efforts have been made on domestic activities classification from
audio recordings, especially the works submitted to the challenge of DCASE
(Detection and Classification of Acoustic Scenes and Events) since 2018. In
contrast, few studies were done on domestic activities clustering, which is a
newly emerging problem. Domestic activities clustering from audio recordings
aims at merging audio clips which belong to the same class of domestic activity
into a single cluster. Domestic activities clustering is an effective way for
unsupervised estimation of daily activities performed in home environment. In
this study, we propose a method for domestic activities clustering using a
convolutional capsule autoencoder network (CCAN). In the method, the deep
embeddings are learned by the autoencoder in the CCAN, while the deep
embeddings which belong to the same class of domestic activities are merged
into a single cluster by a clustering layer in the CCAN. Evaluated on a public
dataset adopted in DCASE-2018 Task 5, the results show that the proposed method
outperforms state-of-the-art methods in terms of the metrics of clustering
accuracy and normalized mutual information.
