PrivacyMic: Utilizing Inaudible Frequencies for Privacy Preserving Daily Activity Recognition

@article{Iravantchi2021PrivacyMicUI,
  title={PrivacyMic: Utilizing Inaudible Frequencies for Privacy Preserving Daily Activity Recognition},
  author={Yasha Iravantchi and Karan Ahuja and Mayank Goel and Chris Harrison and Alanson P. Sample},
  journal={Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
  year={2021}
}
Sound presents an invaluable signal source that enables computing systems to perform daily activity recognition. However, microphones are optimized for human speech and hearing ranges: capturing private content, such as speech, while omitting useful, inaudible information that can aid in acoustic recognition tasks. We simulated acoustic recognition tasks using sounds from 127 everyday household/workplace objects, finding that inaudible frequencies can act as a substitute for privacy-sensitive… 

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