Crowdsensing the speaker count in the wild: implications and applications

The mobile crowdsensing (MCS) paradigm enables large-scale sensing opportunities at lower deployment costs than dedicated infrastructures by utilizing today¿s large number of mobile devices. In the context of MCS, end users with sensing and computing devices can share and extract information of common interest. In this article, we examine Crowd++, an MCS application that accurately estimates the number of people talking in a certain place through unsupervised machine learning analysis on audio segments captured by mobile devices. Such a technique can find application in many domains, such as crowd estimation, social sensing, and personal well being assessment. In this article, we demonstrate the utility of this technique in the context of conference room usage estimation, social diaries, and social engagement in a power-efficient manner followed by a discussion on privacy and possible optimizations to Crowd++ software.