{"title":"基于声学监测技术的鸟类声音识别研究进展:系统回顾","authors":"Daidai Liu, Hanguang Xiao, 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":"{\"title\":\"Research progress in bird sounds recognition based on acoustic monitoring technology: A systematic review\",\"authors\":\"Daidai Liu, Hanguang Xiao, 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}","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}
Research progress in bird sounds recognition based on acoustic monitoring technology: A systematic review
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.
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.