Arik Kershenbaum, Çağlar Akçay, Lakshmi Babu-Saheer, Alex Barnhill, Paul Best, Jules Cauzinille, Dena Clink, Angela Dassow, Emmanuel Dufourq, Jonathan Growcott, Andrew Markham, Barbara Marti-Domken, Ricard Marxer, Jen Muir, Sam Reynolds, Holly Root-Gutteridge, Sougata Sadhukhan, Loretta Schindler, Bethany R Smith, Dan Stowell, Claudia A F Wascher, Jacob C Dunn
{"title":"生物声学研究的自动检测:生物学家和计算机科学家的实用指南。","authors":"Arik Kershenbaum, Çağlar Akçay, Lakshmi Babu-Saheer, Alex Barnhill, Paul Best, Jules Cauzinille, Dena Clink, Angela Dassow, Emmanuel Dufourq, Jonathan Growcott, Andrew Markham, Barbara Marti-Domken, Ricard Marxer, Jen Muir, Sam Reynolds, Holly Root-Gutteridge, Sougata Sadhukhan, Loretta Schindler, Bethany R Smith, Dan Stowell, Claudia A F Wascher, Jacob C Dunn","doi":"10.1111/brv.13155","DOIUrl":null,"url":null,"abstract":"<p><p>Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step-by-step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field.</p>","PeriodicalId":133,"journal":{"name":"Biological Reviews","volume":" ","pages":""},"PeriodicalIF":11.0000,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.\",\"authors\":\"Arik Kershenbaum, Çağlar Akçay, Lakshmi Babu-Saheer, Alex Barnhill, Paul Best, Jules Cauzinille, Dena Clink, Angela Dassow, Emmanuel Dufourq, Jonathan Growcott, Andrew Markham, Barbara Marti-Domken, Ricard Marxer, Jen Muir, Sam Reynolds, Holly Root-Gutteridge, Sougata Sadhukhan, Loretta Schindler, Bethany R Smith, Dan Stowell, Claudia A F Wascher, Jacob C Dunn\",\"doi\":\"10.1111/brv.13155\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. 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Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.
Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step-by-step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field.
期刊介绍:
Biological Reviews is a scientific journal that covers a wide range of topics in the biological sciences. It publishes several review articles per issue, which are aimed at both non-specialist biologists and researchers in the field. The articles are scholarly and include extensive bibliographies. Authors are instructed to be aware of the diverse readership and write their articles accordingly.
The reviews in Biological Reviews serve as comprehensive introductions to specific fields, presenting the current state of the art and highlighting gaps in knowledge. Each article can be up to 20,000 words long and includes an abstract, a thorough introduction, and a statement of conclusions.
The journal focuses on publishing synthetic reviews, which are based on existing literature and address important biological questions. These reviews are interesting to a broad readership and are timely, often related to fast-moving fields or new discoveries. A key aspect of a synthetic review is that it goes beyond simply compiling information and instead analyzes the collected data to create a new theoretical or conceptual framework that can significantly impact the field.
Biological Reviews is abstracted and indexed in various databases, including Abstracts on Hygiene & Communicable Diseases, Academic Search, AgBiotech News & Information, AgBiotechNet, AGRICOLA Database, GeoRef, Global Health, SCOPUS, Weed Abstracts, and Reaction Citation Index, among others.