FuSe: Technologies For Bioacoustic Sensing

GSTDTAP

项目编号	NE/P016677/1
	FuSe: Technologies For Bioacoustic Sensing
	Kate Elizabeth Jones
主持机构	University College London
项目开始年	2017
	2017-04-01
项目结束日期	2018-09-30
资助机构	UK-NERC
项目类别	Research Grant
国家	英国
语种	英语
英文摘要	Biodiversity is facing an unprecedented decline whilst the pressure on the earth's ecosystems continues to grow. Recognising the status of biodiversity and its benefit to human wellbeing, the world's governments committed in 2010 to take effective and urgent action to halt biodiversity loss through the Convention on Biological Diversity's targets. These targets require monitoring to assess progress towards specific goals. Such large-scale biodiversity assessment calls for methods which are able to provide an understanding of large-scale patterns in species' distributions, abundances and changes over time. This relies on surveys to collect data that are representative at a regional to national scale, and robust analysis that is able to provide an informed understanding of species' populations. As a group, bats (Chiroptera) are particularly challenging to monitor because most are nocturnal, wide-ranging and difficult to identify. Historically the monitoring of bats in temperate regions has focused on intensive site-based visual counts or capture surveys. There is considerable value in these approaches, but it is difficult to confidently infer from these what is happening at a wider population level. Acoustic surveys have been used in the UK to monitor bats for the past few decades (e.g. Bat Conservation Trust's National Bat Monitoring Programme), but it is only very recently that advances in sensor technology and analytical acoustic tools have made it possible to identify and monitor more than a handful of easy to identify species. Many of the first open-source tools for automatically detecting and identifying bat species from sound recordings were developed from our previous NERC funded research. We developed acoustic reference libraries for European bat species and the first automatic machine learning classifier. We have since built on this work through our recent EPSRC and Zooniverse funded projects, to make use of the latest machine-learning technologies (Convoluted Neural Networks, CNNs) and through this developed an open-source pipeline for the detection of search-phase echolocation calls and species identification. Despite recent and exciting developments in acoustic species identification, there remain substantial challenges for their cost-effective use within a scalable monitoring tool. A major barrier for deployment at scale is the expense of acoustic sensors, which typically cost up to £1000. As part of this consortium, research at Oxford University has focused on the development of low-cost sensors (http://soundtrap.io), which record uncompressed audio to an SD card. The current sensors are not designed to record at high frequency for bats, but prototype versions, which would cost less than £50 have already been modified and deployed in trials to record bats. The FuSe (Technologies For Bioacoustic Sensing) research consortium brings together this expertise in bat acoustic analysis (University College London), computer science and open source sensor hardware (Oxford University), with large-scale citizen science (British Trust for Ornithology), and national-level bat population monitoring (Bat Conservation Trust). Integrating cutting edge analytical tools for the species identification of bats with the development of new low cost sensors, and expertise in the interpretation of data collected through large-scale volunteer-based acoustic surveys and bat monitoring, we will create an end-to-end open-source system for the large-scale acoustic monitoring of bat populations. This is a catalytic proposal, which has huge implications for the future of bat and bioacoustic monitoring.
来源学科分类	Natural Environment Research
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/86615
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Kate Elizabeth Jones.FuSe: Technologies For Bioacoustic Sensing.2017.