Abstract
A system and method for listener-side, on-demand identification of ambient sounds in occupied environments. The system continuously buffers audio captured through existing consumer devices - smart speakers, televisions, phones - in a rolling temporal window without performing classification, preserving privacy and reducing computational load. When a user initiates a query about a sound they heard, the system isolates the relevant acoustic episode from the buffer, discounts background noise, and classifies the sound using spectral analysis, neural networks, and household-specific contextual history. An interactive refinement loop converts the user's natural-language descriptions of perceived sound qualities into discriminative acoustic constraints that narrow candidate identifications. The system supports multi-modal output for hearing-impaired users, emergency escalation to caregivers, cross-system integration with home surveillance via timecode metadata, reactive microphone reconfiguration after missed detections, and smart-home actuation for confirmed emergencies. Applications include smart-home acoustics, assistive technology for elderly and hearing-impaired individuals, and child safety reassurance.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Zavesky, Eric, "RETROSPECTIVE AMBIENT SOUND IDENTIFICATION VIA LISTENER-INITIATED QUERY, TEMPORAL BUFFERING, AND INTERACTIVE USER-GUIDED DISCRIMINATION", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12446