01691nas a2200145   4500008004100000245012700041210006900168260001600237300001300253490000700266520118100273100002501454700001401479856005201493       2018                        eng d00aRevealing the ecological content of long-duration audio-recordings of the environment through clustering and visualisation0 aRevealing the ecological content of longduration audiorecordings  cJan-03-2018  ae01933450 v133 a<p>Audio recordings of the environment are an increasingly important technique to monitor biodiversity and ecosystem function. While the acquisition of long-duration recordings is becoming easier and cheaper, the analysis and interpretation of that audio remains a significant research area. The issue addressed in this paper is the automated reduction of environmental audio data to facilitate ecological investigations. We describe a method that first reduces environmental audio to vectors of acoustic indices, which are then clustered. This can reduce the audio data by six to eight orders of magnitude yet retain useful ecological information. We describe techniques to visualise sequences of cluster occurrence (using for example, diel plots, rose plots) that assist interpretation of environmental audio. Colour coding acoustic clusters allows months and years of audio data to be visualised in a single image. These techniques are useful in identifying and indexing the contents of long-duration audio recordings. They could also play an important role in monitoring long-term changes in species abundance brought about by habitat degradation and/or restoration.</p>
1 aPhillips, Yvonne, F.1 aRoe, Paul  uhttp://dx.plos.org/10.1371/journal.pone.019334501053nas a2200145   4500008004100000245009600041210006900137520054000206653002500746653002100771653002800792653002300820100002500843856003900868       2017                        eng d00aThe Clustering of Acoustic Indices derived from Long-duration Recordings of the Environment0 aClustering of Acoustic Indices derived from Longduration Recordi3 a<p>This paper outlines the recording dataset and methods used to choose a clustering algorithm for a large twenty-six month acoustic dataset. The recordings were of the natural environment and consist of thirteen months of recording from each of two sites in two national parks 160 km north of Brisbane, Queensland. This paper also explains the calculation and use of the intra-three-day-distance (I3DD) error measure used to determine the optimum clustering result. Site maps and photos are provided at the end of this document.</p>
10aacoustic environment10aacoustic indices10ahierarchical clustering10ak-means clustering1 aPhillips, Yvonne, F.  uhttps://eprints.qut.edu.au/110659/