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Scalable preprocessing of high volume environmental acoustic data for bioacoustic monitoring

Citation

Brown, A and Garg, S and Montgomery, J, Scalable preprocessing of high volume environmental acoustic data for bioacoustic monitoring, PLoS One, 13, (8) Article e0201542. ISSN 1932-6203 (2018) [Refereed Article]


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Copyright Statement

Copyright 2018 Brown et al. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1371/journal.pone.0201542

Abstract

In this work, we examine the problem of efficiently preprocessing and denoising high volume environmental acoustic data, which is a necessary step in many bird monitoring tasks. Preprocessing is typically made up of multiple steps which are considered separately from each other. These are often resource intensive, particularly because the volume of data involved is high. We focus on addressing two challenges within this problem: how to combine existing preprocessing tasks while maximising the effectiveness of each step, and how to process this pipeline quickly and efficiently, so that it can be used to process high volumes of acoustic data. We describe a distributed system designed specifically for this problem, utilising a master-slave model with data parallelisation. By investigating the impact of individual preprocessing tasks on each other, and their execution times, we determine an efficient and accurate order for preprocessing tasks within the distributed system. We find that, using a single core, our pipeline executes 1.40 times faster compared to manually executing all preprocessing tasks. We then apply our pipeline in the distributed system and evaluate its performance. We find that our system is capable of preprocessing bird acoustic recordings at a rate of 174.8 seconds of audio per second of real time with 32 cores over 8 virtual machines, which is 21.76 times faster than a serial process.

Item Details

Item Type:Refereed Article
Keywords:cloud computing, distributed system, big data, bioacoustics
Research Division:Information and Computing Sciences
Research Group:Distributed Computing
Research Field:Distributed and Grid Systems
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Application Tools and System Utilities
UTAS Author:Brown, A (Mr Alexander Brown)
UTAS Author:Garg, S (Dr Saurabh Garg)
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:127424
Year Published:2018
Deposited By:Information and Communication Technology
Deposited On:2018-07-27
Last Modified:2019-02-26
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