Domestic Activity Clustering from Audio via Depthwise Separable Convolutional Autoencoder Network

Yanxiong Li, Wenchang Cao, Konstantinos Drossos, Tuomas Virtanen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


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:

Original languageEnglish
Title of host publication2022 IEEE 24th International Workshop on Multimedia Signal Processing, MMSP 2022
Number of pages6
ISBN (Electronic)9781665471893
Publication statusPublished - 2022
Publication typeA4 Article in conference proceedings
EventIEEE International Workshop on Multimedia Signal Processing - Shanghai, China
Duration: 26 Sept 202228 Sept 2022

Publication series

NameIEEE International Workshop on Multimedia Signal Processing
ISSN (Electronic)2473-3628


ConferenceIEEE International Workshop on Multimedia Signal Processing


  • depthwise separable convolutional autoencoder
  • domestic activity clustering
  • human activity estimation

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Media Technology


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