Inference of conversation partners by cooperative acoustic sensing in smartphone networks

Yan Ann Chen*, Ji Chen, Yu-Chee Tseng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish
Article number7181724
Pages (from-to)1387-1400
Number of pages14
JournalIEEE Transactions on Mobile Computing
Volume15
Issue number6
DOIs
StatePublished - 1 Jun 2016

Keywords

  • acoustic sensing
  • conversation inference
  • cooperative sensing
  • pervasive computing
  • smartphone
  • social interaction

Fingerprint Dive into the research topics of 'Inference of conversation partners by cooperative acoustic sensing in smartphone networks'. Together they form a unique fingerprint.

Cite this