An architecture for Cloud-based bioacoustics workflow processing is proposed.
The architecture takes advantage of distinct properties of bioacoustics workflows.
A prototype outperforms a similar generic system in testing and is scalable.
There has been increasing interest in using automated bioacoustics analysis to monitor the environment. This involves using computational approaches to identify animals and other environmental phenomena from the sounds that they generate. The volume of data being recorded for bioacoustics analyses is increasing, as the scale of environmental surveys is increasing. This presents significant computational demands to perform analyses. These large-scale analyses cannot be performed at feasible speeds using traditional computing approaches. This research proposes AcoustiCloud: a system framework which represents bioacoustics processes as workflows and executes these across a Cloud-based system. It enables fast and efficient bioacoustics analysis for a variety of scenarios. The proposed system considers characteristics specific to bioacoustics processes resulting in fast execution times and high scalability. An implemented prototype is found to execute a bioacoustics workflow with 10 min of audio over 10 times faster than Pegasus, a widely used Workflow Management System.