abstract: 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. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2018
| Identifer | oai:union.ndltd.org:asu.edu/item:51586 |
| Date | January 2018 |
| Contributors | Pressler, Daniel (Author), Bliss, Daniel W (Advisor), Berisha, Visar (Committee member), Corman, Steven (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 64 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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