Fingerprinting to Identify Repeated Sound Events in Long-Duration Personal Audio Recordings

Body-worn solid-state audio recorders can easily and cheaply capture the bearer's entire acoustic environment throughout the day; we refer to such recordings as "personal audio". Extracting useful information, and providing access and navigation tools for this data is a challenge; in this paper we investigate the use of an audio fingerprinting technique, originally developed for identifying music recordings corrupted by noise, as a tool to rapidly identify recurrent sound events within long (multi-day) recordings. The fingerprinting technique is based on energy peaks in time-frequency, largely removing framing issues and making it intrinsically robust to background noise levels. We show that the technique is very effective at identifying exact repetitions of structured sound (such as jingles and electronic telephone rings) but is unable to find repeats of more 'organic' sound events such as garage door openings.