ReviewBiological reviews of the Cambridge Philosophical Society2025
Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.
Review in Biological reviews of the Cambridge Philosophical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification.The Journal of animal ecology · 2025Pooled it
- Real-Time Segmentation and Classification of Birdsong Syllables for Learning Experiments.eNeuro · 2026Article
- Acoustic Features and Recognition of Distress Calls inBiology · 2026Article
- Ecology Careers in the AI Era: Early-Career Data Literacy Must Be Protected.Ecology and evolution · 2026Article
- Passive acoustic monitoring can provide insights into occupancy dynamics and impacts of disturbance for at-risk species.Ecological applications : a publication of the Ecological Society of America · 2026Article
- Roar Data: Redefining a Lion's Roar Using Machine Learning.Ecology and evolution · 2025Article
- Automating timeScientific reports · 2025Article
- Environmental drivers of calling activity in the critically endangered lemur leaf frog,Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
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Authors and funding
22 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.