Privacy-aware environmental sound classification for indoor human activity recognition

@article{Wang2019PrivacyawareES,
  title={Privacy-aware environmental sound classification for indoor human activity recognition},
  author={Wei Wang and Fatjon Seraj and Nirvana Meratnia and Paul J. M. Havinga},
  journal={Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments},
  year={2019}
}
  • Wei Wang, F. Seraj, P. Havinga
  • Published 5 June 2019
  • Computer Science
  • Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments
This paper presents a comparative study on different feature extraction and machine learning techniques for indoor environmental sound classification. Compared to outdoor environmental sound classification systems, indoor systems need to pay special attention to power consumption and privacy. We consider feature calculation complexity, classification accuracy and privacy as evaluation metrics. To ensure privacy, we strip voice bands from sound input to make human conversations unrecognizable… 
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