Abstract
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.
Highlights
Interpreting long-duration acoustic recordings of the natural environment has become an important technique for ecologists wishing to monitor terrestrial ecosystems
The maximum Silhouette value of 0.14 at five clusters was well below the 0.25 threshold, usually interpreted to imply that there is “no substantial structure” in the data [31 p.343]. Because these indices did not provide clear optimum values for k1 and k2, we turned to the formulation of the I3DD ‘error’ function derived from acoustic signatures of days having maximum biophony
We have described a two-step hybrid clustering technique which greatly compresses audio data while retaining ecologically relevant acoustic information
Summary
Interpreting long-duration acoustic recordings of the natural environment has become an important technique for ecologists wishing to monitor terrestrial ecosystems. Persistence: acoustic data can be stored for later analysis when new techniques become available; 3. The availability of terabytes of acoustic data has spawned a new discipline, ecoacoustics, which inherits its theoretical and methodological insights from two existing disciplines, bioacoustics and landscape ecology [1]. Technology has made acoustic recordings available, but ecologists are unable to listen to all the collected audio [2]. Ecoacoustics operates on large temporal and spatial scales. It treats the soundscape as a dynamic acoustic environment, both created by and influencing the behaviour of its embedded local fauna [3, 4]. Noise (BGN): Calculated as described in Towsey [21]. Indices 5, 6, and 7 are derived from the noise-reduced decibel spectrogram as described in [21]
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