Bioacoustics Data Analysis – A Taxonomy, Survey and Open Challenges

Published on Jan 1, 2020in IEEE Access4.098
· DOI :10.1109/ACCESS.2020.2978547
Rama Rao Kvsn (UTAS: University of Tasmania), James Montgomery13
Estimated H-index: 13
(UTAS: University of Tasmania)
+ 1 AuthorsMichael Charleston
Biodiversity monitoring has become a critical task for governments and ecological research agencies for reducing significant loss of animal species. Existing monitoring methods are time-intensive and techniques such as tagging are also invasive and may adversely affect animals. Bioacoustics based monitoring is becoming an increasingly prominent non-invasive method, involving the passive recording of animal sounds. Bioacoustics analysis can provide deep insights into key environmental integrity issues such as biodiversity, density of individuals and present or absence of species. However, analysing environmental recordings is not a trivial task. In last decade several researchers have tried to apply machine learning methods to automatically extract insights from these recordings. To help current researchers and identify research gaps, this paper aims to summarise and classify these works in the form of a taxonomy of the various bioacoustics applications and analysis approaches. We also present a comprehensive survey of bioacoustics data analysis approaches with an emphasis on bird species identification. The survey first identifies common processing steps to analyse bioacoustics data. As bioacoustics monitoring has grown, so does the volume of raw acoustic data that must be processed. Accordingly, this survey examines how bioacoustics analysis techniques can be scaled to work with big data. We conclude with a review of open challenges in the bioacoustics domain, such as multiple species recognition, call interference and automatic selection of detectors.
  • References (102)
  • Citations (0)
📖 Papers frequently viewed together
1 Citations
1 Citations
78% of Scinapse members use related papers. After signing in, all features are FREE.
#1Xianjun Xia (UWA: University of Western Australia)H-Index: 5
#2Roberto Togneri (UWA: University of Western Australia)H-Index: 22
Last. Defeng Huang (UWA: University of Western Australia)H-Index: 15
view all 5 authors...
Recently, neural network-based deep learning methods have been popularly applied to computer vision, speech signal processing and other pattern recognition areas. Remarkable success has been demonstrated by using the deep learning approaches. The purpose of this article is to provide a comprehensive survey for the neural network-based deep learning approaches on acoustic event detection. Different deep learning-based acoustic event detection approaches are investigated with an emphasis on both s...
Birds are an important group of animal that ecologist monitor using autonomous recordings units as an crucial indicator of health of an environment. There is not yet an adequate method for automated bird call recognition in acoustic recordings due to high variations in bird calls and the challenges associated with bird call recognition. In this paper, we use ResNet-50, a deep convolutional neural network architecture for automated bird call recogni- tion. We used a publicly available dataset con...
4 Citations
#1Botond FazekasH-Index: 1
Last. Andreas RauberH-Index: 35
view all 4 authors...
5 Citations
#1Arik Kershenbaum (University of Cambridge)H-Index: 12
#2Daniel T. Blumstein (UCLA: University of California, Los Angeles)H-Index: 65
Last. Veronica Zamora-Gutierrez (University of Cambridge)H-Index: 7
view all 42 authors...
This review was developed at an investigative workshop, “Analyzing Animal Vocal Communication Sequences” that took place on October 21–23 2013 in Knoxville, Tennessee, sponsored by the National Institute for Mathematical and Biological Synthesis (NIMBioS). NIMBioS is an Institute sponsored by the National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Department of Agriculture through NSF Awards #EF-0832858 and #DBI-1300426, with additional support from The University...
77 CitationsSource
Recent estimates of the number of species inahabiting tropical forests, exceed those described scientifically by one order of magnitude. This diversity is threatened by the ongoing rapid destruction of tropical habitats and has led to the necessity for quick surveys to identify biodiversity rich areas. Sound recordings can represent a valuable tool for tropical areas under threat. Recordings from an amazon lowland forest are analyzed and sound patterns, mainly generated by crickets (Gryllidae), ...
59 Citations
#1Juan Gabriel Colonna (UFAM: Federal University of Amazonas)H-Index: 7
#2Marco Cristo (UFAM: Federal University of Amazonas)H-Index: 17
Last. Eduardo F. Nakamura (UFAM: Federal University of Amazonas)H-Index: 13
view all 4 authors...
An incremental transformation of ZCR and energy without using temporal windows.With our method is possible to save memory and transmission costs.Solution to process large amounts of data by resource-constrained devices as WSN. A bioacoustical animal recognition system is composed of two parts: (1) the segmenter, responsible for detecting syllables (animal vocalization) in the audio; and (2) the classifier, which determines the species/animal whose the syllables belong to. In this work, we first ...
25 CitationsSource
#1Peter J (Cornell University)H-Index: 1
#2Dugan (Cornell University)H-Index: 1
Last. Christopher W. Clark (Cornell University)H-Index: 37
view all 5 authors...
This paper discusses a new algorithm, called theacoustic data-mining accelerator (ADA), which was developedto mine large sound archives for signals of interest includinganimal vocalizations. Background information on thedevelopment of ADA is provided, summarizing variousprojects that have utilized this technology since 2009. Performance was evaluated by comparing runtimes andefficiency metrics for two marine mammal detectionalgorithms that were applied to a 3-week single channelacoustic data set...
6 CitationsSource
#1Mangalam Sankupellay (UQ: University of Queensland)H-Index: 5
#2Michael Towsey (UQ: University of Queensland)H-Index: 19
Last. Paul Roe (UQ: University of Queensland)H-Index: 22
view all 4 authors...
Acoustic recordings play an increasingly important role in monitoring terrestrial environments. However, due to rapid advances in technology, ecologists are accumulating more audio than they can listen to. Our approach to this big-data challenge is to visualize the content of long-duration audio-recordings by calculating acoustic indices. These are statistics which describe the temporal-spectral distribution of acoustic energy and reflect content of ecological interest. We combine spectral indic...
9 CitationsSource
This paper presents a new software model designed for distributed sonic signal detection runtime using machine learning algorithms called DeLMA. A new algorithm--Acoustic Data-mining Accelerator (ADA)--is also presented. ADA is a robust yet scalable solution for efficiently processing big sound archives using distributing computing technologies. Together, DeLMA and the ADA algorithm provide a powerful tool currently being used by the Bioacoustics Research Program (BRP) at the Cornell Lab of Orni...
11 Citations
#1Omar Y. Al-Jarrah (Khalifa University)H-Index: 8
#2Paul D. Yoo (BU: Bournemouth University)H-Index: 16
Last. Kamal Taha (Khalifa University)H-Index: 10
view all 5 authors...
With the emerging technologies and all associated devices, it is predicted that massive amount of data will be created in the next few years - in fact, as much as 90% of current data were created in the last couple of years - a trend that will continue for the foreseeable future. Sustainable computing studies the process by which computer engineer/scientist designs computers and associated subsystems efficiently and effectively with minimal impact on the environment. However, current intelligent...
145 CitationsSource
Cited By0