Abstract-The Mobile Crowd Sensing (MCS) paradigm enables large-scale sensing opportunities at lower deployment costs than dedicated infrastructures by utilizing the large number of today's 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++, a MCS application, which 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 diary, and social engagement in a power efficient manner followed by a discussion on privacy and possible optimizations to Crowd++ software
