Honorable Mention
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 frequencies. To take advantage of these inaudible frequencies, we designed a Raspberry Pi-based device that captures inaudible acoustic frequencies with settings that can remove speech or all audible frequencies entirely. We conducted a perception study, where participants "eavesdropped" on PrivacyMic’s filtered audio and found that none of our participants could transcribe speech. Finally, PrivacyMic’s real-world activity recognition performance is comparable to our simulated results, with over 95% classification accuracy across all environments, suggesting immediate viability in performing privacy-preserving daily activity recognition.
https://doi.org/10.1145/3411764.3445169
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)