  In this work, we examine the problem of efficiently preprocessing high volume
bird acoustic data. We combine several existing preprocessing steps including
noise reduction approaches into a single efficient pipeline by examining each
process individually. We then utilise a distributed computing architecture to
improve execution time. Using a master-slave model with data parallelisation,
we developed a near-linear automated scalable system, capable of preprocessing
bird acoustic recordings 21.76 times faster with 32 cores over 8 virtual
machines, compared to a serial process. This work contributes to the research
area of bioacoustic analysis, which is currently very active because of its
potential to monitor animals quickly at low cost. Overcoming noise interference
is a significant challenge in many bioacoustic studies, and the volume of data
in these studies is increasing. Our work makes large scale bird acoustic
analyses more feasible by parallelising important bird acoustic processing
tasks to significantly reduce execution times.
