A human communications research project at Arizona State University aurally

recorded the daily interactions of aware and consenting employees and their visiting

clients at the Software Factory, a software engineering consulting team, over a three

year period. The resulting dataset contains valuable insights on the communication

networks that the participants formed however it is far too vast to be processed manually

by researchers. In this work, digital signal processing techniques are employed

to develop a software toolkit that can aid in estimating the observable networks contained

in the Software Factory recordings. A four-step process is employed that starts

with parsing available metadata to initially align the recordings followed by alignment

estimation and correction. Once aligned, the recordings are processed for common

signals that are detected across multiple participants’ recordings which serve as a

proxy for conversations. Lastly, visualization tools are developed to graphically encode

the estimated similarity measures to efficiently convey the observable network

relationships to assist in future human communications research.