ReviewPeerJ2022
Computational bioacoustics with deep learning: a review and roadmap.
Review in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
78 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Software Tools for Passive Acoustic Monitoring in Aquatic and Terrestrial Bio- and Ecoacoustics: A Living Systematic Review.F1000Research · 2026Pooled it
- AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring.Sensors (Basel, Switzerland) · 2025Pooled it
- BATSY4-PRO: an open-source multichannel ultrasound recorder for field bioacoustics.BMC ecology and evolution · 2026Article
- Searching for separation between frequency-modulated calls of blue and fin whales.Scientific reports · 2026Article
- Twelve quick tips for applying deep learning to animal sounds.PLoS computational biology · 2026Article
- PeaDetect dataset: Curated audio recordings of Indian peafowl (Pavo cristatus) vocalizations for bioacoustic monitoring.Data in brief · 2026Article
- Design Patterns for the Development and Implementation of Bioacoustic Deep Learning Recognizers.Ecology and evolution · 2026Article
- Quality-Controlled Generative Augmentation for North Atlantic Right Whale Upcall Detection Using Contour 1D-VAE.Sensors (Basel, Switzerland) · 2026Article
- Overcoming Data Scarcity: Few-Shot Pig Vocalization Recognition via Domain Expansion, Knowledge Transfer, and Feature Alignment.Animals : an open access journal from MDPI · 2026Article
- Deep Audio Detection Networks From Passive Acoustic Monitoring of a Critically Endangered Primate.Integrative zoology · 2026Article
- Real-Time Segmentation and Classification of Birdsong Syllables for Learning Experiments.eNeuro · 2026Article
- A Sensor-Based TinyML Acoustic Monitoring System for Edge-Side Animal Sound Recognition on Resource-Constrained Microcontrollers.Sensors (Basel, Switzerland) · 2026Article
- Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.PLoS computational biology · 2026Article
- Passive Acoustic Monitoring and Deep Learning Reveal a Lag From Rainfall to Gibbon Song Across a Mosaic Forest Landscape.Ecology and evolution · 2026Article
- National-scale acoustic monitoring of avian biodiversity and migration.Communications biology · 2026Article
- Acoustic Features and Recognition of Distress Calls inBiology · 2026Article
- A fully automated framework for acoustic identification and localization of terrestrial wildlife at scale.Communications biology · 2026Article
- Building trust with marginalized communities in participatory acoustic monitoring through dynamic consent.Conservation biology : the journal of the Society for Conservation Biology · 2026Article
- A dataset of insect sounds from 459 species for bioacoustic machine learning.Scientific data · 2026Article
- Contrastive learning for passive acoustic monitoring: A framework for sound source discovery and cross-site comparison in marine soundscapes.PLoS computational biology · 2026Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Animal vocalisations and natural soundscapes are fascinating objects of study, and contain valuable evidence about animal behaviours, populations and ecosystems. They are studied in bioacoustics and ecoacoustics, with signal processing and analysis an important component. Computational bioacoustics has accelerated in recent decades due to the growth of affordable digital sound recording devices, and to huge progress in informatics such as big data, signal processing and machine learning. Methods are inherited from the wider field of deep learning, including speech and image processing. However, the tasks, demands and data characteristics are often different from those addressed in speech or music analysis. There remain unsolved problems, and tasks for which evidence is surely present in many acoustic signals, but not yet realised. In this paper I perform a review of the state of the art in deep learning for computational bioacoustics, aiming to clarify key concepts and identify and analyse knowledge gaps. Based on this, I offer a subjective but principled roadmap for computational bioacoustics with deep learning: topics that the community should aim to address, in order to make the most of future developments in AI and informatics, and to use audio data in answering zoological and ecological questions.
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What OpenQuestion holds
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.