Privacymic: Utilizing inaudible frequencies for privacy preserving daily activity recognition

Abstract

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 house-hold/workplace objects, fnding 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 fltered 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% classifcation accuracy across all environments, suggesting immediate viability in performing privacy-preserving daily activity recognition.