Research progress in bird sounds recognition based on acoustic monitoring technology: A systematic review

IF 3.4 2区 物理与天体物理 Q1 ACOUSTICS
Daidai Liu, Hanguang Xiao, Kai Chen
{"title":"Research progress in bird sounds recognition based on acoustic monitoring technology: A systematic review","authors":"Daidai Liu,&nbsp;Hanguang Xiao,&nbsp;Kai Chen","doi":"10.1016/j.apacoust.2024.110285","DOIUrl":null,"url":null,"abstract":"<div><div>Bird sound contains rich ecological information, and its related research results can be applied to animal behavior analysis, natural information collection and ecological environment monitoring. Since the early manual monitoring methods, many researchers have continuously innovated and improved the bird sounds recognition technology to overcome the long-standing drawbacks of long cycle time, high cost and poor effectiveness. These developments make bird sounds recognition a highly interesting, but also highly challenging research topic. Acoustic monitoring technology plays a vital role in the automatic recognition of bird sounds. With the popularization of acoustic monitoring technology, the technical routes based on traditional recognition models and neural networks have increased sharply, which has greatly promoted the development of bird sounds recognition. In view of these main technical routes, this paper summarized the research status of bird sounds recognition, provided a summary table of a variety of bird sound sample datasets, introduced the application evolution of various recognition technologies, and analyzed its open challenges. Meanwhile, this paper also cited the published experimental exploration on the improvement of deep learning networks. In general, this paper gives a comprehensive overview of the research process of bird sounds recognition based on acoustic monitoring technology, which has important theoretical and practical value to promote the development of bird sounds recognition technology, and provides a valuable reference for future related research.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Acoustics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0003682X24004365","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0

Abstract

Bird sound contains rich ecological information, and its related research results can be applied to animal behavior analysis, natural information collection and ecological environment monitoring. Since the early manual monitoring methods, many researchers have continuously innovated and improved the bird sounds recognition technology to overcome the long-standing drawbacks of long cycle time, high cost and poor effectiveness. These developments make bird sounds recognition a highly interesting, but also highly challenging research topic. Acoustic monitoring technology plays a vital role in the automatic recognition of bird sounds. With the popularization of acoustic monitoring technology, the technical routes based on traditional recognition models and neural networks have increased sharply, which has greatly promoted the development of bird sounds recognition. In view of these main technical routes, this paper summarized the research status of bird sounds recognition, provided a summary table of a variety of bird sound sample datasets, introduced the application evolution of various recognition technologies, and analyzed its open challenges. Meanwhile, this paper also cited the published experimental exploration on the improvement of deep learning networks. In general, this paper gives a comprehensive overview of the research process of bird sounds recognition based on acoustic monitoring technology, which has important theoretical and practical value to promote the development of bird sounds recognition technology, and provides a valuable reference for future related research.
基于声学监测技术的鸟类声音识别研究进展:系统回顾
鸟声蕴含着丰富的生态信息,其相关研究成果可应用于动物行为分析、自然信息采集和生态环境监测等领域。从早期的人工监测方法开始,许多研究人员不断创新和改进鸟声识别技术,以克服长期以来存在的周期长、成本高、效果差等弊端。这些发展使鸟声识别成为一个非常有趣但也极具挑战性的研究课题。声学监测技术在鸟声自动识别中起着至关重要的作用。随着声学监测技术的普及,基于传统识别模型和神经网络的技术路线急剧增加,极大地推动了鸟声识别的发展。针对这些主要技术路线,本文总结了鸟声识别的研究现状,提供了多种鸟声样本数据集汇总表,介绍了各种识别技术的应用演进,并分析了其面临的挑战。同时,本文还引用了已发表的关于改进深度学习网络的实验探索。总的来说,本文全面概述了基于声学监测技术的鸟声识别研究过程,对推动鸟声识别技术的发展具有重要的理论和实践价值,并为今后的相关研究提供了有价值的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Applied Acoustics
Applied Acoustics 物理-声学
CiteScore
7.40
自引率
11.80%
发文量
618
审稿时长
7.5 months
期刊介绍: Since its launch in 1968, Applied Acoustics has been publishing high quality research papers providing state-of-the-art coverage of research findings for engineers and scientists involved in applications of acoustics in the widest sense. Applied Acoustics looks not only at recent developments in the understanding of acoustics but also at ways of exploiting that understanding. The Journal aims to encourage the exchange of practical experience through publication and in so doing creates a fund of technological information that can be used for solving related problems. The presentation of information in graphical or tabular form is especially encouraged. If a report of a mathematical development is a necessary part of a paper it is important to ensure that it is there only as an integral part of a practical solution to a problem and is supported by data. Applied Acoustics encourages the exchange of practical experience in the following ways: • Complete Papers • Short Technical Notes • Review Articles; and thereby provides a wealth of technological information that can be used to solve related problems. Manuscripts that address all fields of applications of acoustics ranging from medicine and NDT to the environment and buildings are welcome.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信