FuSe: Technologies For Bioacoustic Sensing

FuSe：生物声学传感技术

• 批准号: NE/P016677/1
• 负责人: Kate Jones
• 金额: $ 12.7万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FP016677%2F1
• 关键词: FuSe; Technologies; Bioacoustic; Sensing

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.

生物多样性正面临着前所未有的下降，而地球生态系统的压力继续增加。认识到生物多样性的现状及其对人类福祉的好处，世界各国政府在2010年承诺采取有效和紧急行动，通过《生物多样性公约》的目标阻止生物多样性的丧失。这些目标需要监测，以评估实现具体目标的进展情况。这种大规模的生物多样性评估需要能够了解物种分布、丰度和随时间变化的大规模模式的方法。这依赖于调查来收集在区域到国家范围内具有代表性的数据，以及能够提供对物种种群的知情了解的强大分析。作为一个群体，蝙蝠（翼手目）特别具有挑战性，因为大多数蝙蝠都是夜间活动，分布广泛，难以识别。从历史上看，温带地区的蝙蝠监测集中在密集的基于站点的视觉计数或捕获调查。这些方法有相当大的价值，但很难有把握地从这些方法中推断出在更广泛的人口水平上发生了什么。在过去的几十年里，声学调查已经在英国用于监测蝙蝠（例如蝙蝠保护信托基金的国家蝙蝠监测计划），但直到最近，传感器技术和分析声学工具的进步才使识别和监测超过少数容易识别的物种成为可能。许多第一个从录音中自动检测和识别蝙蝠物种的开源工具都是从我们以前的NERC资助的研究中开发出来的。我们为欧洲蝙蝠物种开发了声学参考库，并开发了第一个自动机器学习分类器。此后，我们通过最近的EPSRC和Zooniverse资助项目，利用最新的机器学习技术，（卷积神经网络，CNN），并通过这一点开发了一个开源管道，用于检测搜索阶段回声定位呼叫和物种识别。尽管最近在声学物种识别方面取得了令人兴奋的进展，在可扩展的监测工具中，它们的成本效益使用仍然存在重大挑战。大规模部署的一个主要障碍是声学传感器的费用，通常成本高达1000英镑。作为该联盟的一部分，牛津大学的研究重点是开发低成本传感器（soundtrap.io），该传感器将未压缩的音频记录到SD卡中。目前的传感器并不是为了记录蝙蝠的高频率而设计的，但是原型版本，成本不到50英镑，已经被修改并部署在试验中记录蝙蝠。FuSe（生物声学传感技术）研究联合会将蝙蝠声学分析（伦敦大学学院）、计算机科学和开放源传感器硬件（牛津大学）、大规模公民科学（英国鸟类学信托基金）和国家一级蝙蝠种群监测（蝙蝠保护信托基金）方面的专门知识结合在一起。将用于蝙蝠物种识别的尖端分析工具与新的低成本传感器的开发相结合，以及通过大规模基于志愿者的声学调查和蝙蝠监测收集的数据解释方面的专业知识，我们将创建一个端到端的开源系统，用于蝙蝠种群的大规模声学监测。这是一个催化性的提议，对蝙蝠和生物声学监测的未来有着巨大的影响。

项目成果

Accounting for spatial autocorrelation and environment are important to derive robust bat population trends from citizen science data

考虑空间自相关和环境对于从公民科学数据中得出稳健的蝙蝠种群趋势非常重要

• DOI: 10.1016/j.ecolind.2022.108719
• 发表时间: 2022
• 期刊: Ecological Indicators
• 影响因子: 6.9
• 作者: Browning E

CityNet - Deep Learning Tools for Urban Ecoacoustic Assessment

CityNet - 用于城市生态声学评估的深度学习工具

• DOI: 10.1101/248708
• 发表时间: 2018
• 作者: Fairbrass A

MAMMALS IN PORTUGAL: A data set of terrestrial, volant, and marine mammal occurrences in Portugal.

葡萄牙的哺乳动物：葡萄牙陆地、飞行和海洋哺乳动物发生情况的数据集。

• DOI: 10.1002/ecy.3654
• 发表时间: 2022
• 期刊: Ecology
• 影响因子: 4.8
• 作者: Grilo C

CityNet-Deep learning tools for urban ecoacoustic assessment

• DOI: 10.1111/2041-210x.13114
• 发表时间: 2019-02-01
• 期刊: METHODS IN ECOLOGY AND EVOLUTION
• 影响因子: 6.6
• 作者: Fairbrass, Alison J.;Firman, Michael;Jones, Kate E.
• 通讯作者: Jones, Kate E.

Biases of acoustic indices measuring biodiversity in urban areas

• DOI: 10.1016/j.ecolind.2017.07.064
• 发表时间: 2017-12-01
• 期刊: ECOLOGICAL INDICATORS
• 影响因子: 6.9
• 作者: Fairbrass, Alison J.;Rennett, Peter;Jones, Kate E.
• 通讯作者: Jones, Kate E.