Inference of Conversation Partners by Cooperative Acoustic Sensing in Smartphone Networks

A lot of personal daily contexts and activities may be inferred by analyzing acoustic signals in vicinity. Conversations play an important role in one’s social communications. In this work, we consider the inference of conversation partners via acoustic sensing conducted by a group of smartphones in vicinity. By considering the continuity and overlap of speeches, we propose novel inference methods to identify conversational relationships among co-located users. In our system, each smartphone individually processes the acoustic data to understand its owner’s talking turns and emotions.

Via direct wireless communications, smartphones then cooperatively conduct the inference to retrieve conversational groups. Compared to existing work, which only exploits peer-to-peer conversational relationships, our approach is able to capture group conversational relationships in a more real-time manner. A prototype on Android smartphones is demonstrated to verify the feasibility of our approach. We also collect conversation data from movie clips and real life with 2 to 14 speakers to validate our result, which shows promising performance.