An Empirical Study of Collaborative Acoustic Source Localization

Field biologists use animal sounds to discover the presence of individuals and to study their behavior. Collecting bio- acoustic data has traditionally been a difficult and time- consuming process in which researchers use portable microphones to record sounds while taking notes of their own detailed observations. The recent development of new deploy- able acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we implement an AML-based source localization algorithm, and use it to localize marmot alarm-calls. We assess the performance of these techniques based on results from two field experiments: (1) a controlled test of direction-of- arrival (DOA) accuracy using a pre-recorded source signal, and (2) an experiment to detect and localize actual animals in their habitat, with a comparison to ground truth gathered from human observations. Although small arrays yield ambiguities from spatial aliasing of high frequency signals, we show that these ambiguities are readily eliminated by proper bearing crossings of the DOAs from several arrays. These results show that the AML source localization algorithm can be used to localize actual animals in their natural habitat, using a platform that is practical to deploy.