It is common to be carrying an advanced computational device with a microphone a smartphone on your person at virtually all times. One application this makes possible is to automatically detect when individuals are in close proximity by detecting the similarity between the acoustic ambience recorded by body-worn mics. This paper investigates two techniques for proximity detection on a database of personal audio recordings made by six participants in a poster presentation session. We show that cross-correlation between 10 s windows is effective for detecting when individuals are close enough to be in conversation, and that using a fingerprinting approach based on acoustic landmarks is comparably accurate for this task, while at the same time being much more efficient, privacy-preserving, and viable for detecting proximity between a large number of body-worn devices.
Bibliographic reference. Ellis, Daniel P. W. / Satoh, Hiroyuki / Chen, Zhuo (2014): "Detecting proximity from personal audio recordings", In INTERSPEECH-2014, 2519-2523.