Determining co-location using a sequential hypothesis test on patterns of silence

In everyday meetings, automatic association of co-located mobile devices would ease sharing of web-links, media, and other information. We propose a method that compares patterns of silence from device microphones to detect co-location of those devices. This method works with unsynchronized audio capture, requires only 100bps and preserves privacy. We show how to formulate pattern matching in a sequential hypothesis framework so that changes in co-location status (when people leave or join a meeting) can be determined promptly, and how to compute the likelihood ratio in practice. Using 16 hours of captured audio, we show that our approach can correctly determine device co-location with a low error rate of 0.05%, and can detect co-location changes 10 seconds faster than a similar decision rule based on a constant time window. Compared to a prior audio signature method, we achieve higher accuracy at 1/7 the bit rate.