Abstract by William Wright
Coordinated Persistent Homology in Analysis of Avian Vocalizations
In recent years, many new data analysis algorithms have been developed that employ Persistent Homology to extract previously unobtainable patterns from data. However, Persistent Homology algorithms are limited in their usefulness because they can only analyze one parameter at a time. Other algorithms have been developed in to analyze multiple parameters at once (these are referred to as Multiple Persistent Homology algorithms), but as it turns out they are not feasibly computable given any realistic data set. Here we employ a compromise technique (know as Coordinated Persistent Homology) developed by Nick Callor and Gregory Conner at BYU to analyze avian vocalizations in an attempt to differentiate between spiecies.