An acoustic-based encounter profiling system - Zhang, Huanle (2020)
This paper presents DopEnc, an acoustic-based encounter profiling system on commercial off-the-shelf smartphones. DopEnc automatically identifies the persons that users interact with in the context of encountering. DopEnc performs encounter profiling in two major steps: (1) Doppler profiling to detect that two persons approach and stop in front of each other via an effective trajectory, and (2) voice profiling to confirm that they are thereafter engaged in an interactive conversation. DopEnc is further extended to support parallel acoustic exploration of many users by incorporating a unique multiple access scheme within the limited inaudible acoustic frequency band. All implementation of DopEnc is based on commodity sensors like speakers, microphones, and accelerometers integrated on mainstream smartphones. We evaluate DopEnc with detailed experiments and a real use-case study of 11 participants. Overall DopEnc achieves an accuracy of 6.9 percent false positive and 9.7 percent false negative in real usage.