BioacAI develops new AI methods directly within the context of acoustic wildlife monitoring. It has a cross-disciplinary span, which can be divided into four themes: AI methods, on-device algorithms, behaviour analysis and ecological monitoring. The project is structured to progress on all four of these in parallel, and to make connections between them. Progress on all four is proceeding as planned.
Hardware and devices: Some parts of the project use existing devices, such as the well-known Audiomoth sound recorder. Others have been developing new or refined hardware designs: in particular, designs and strategies for 3D spatial analysis of sound patterns, through techniques such as beamforming and sound source localisation, using multi-microphone “arrays”. Prototypes have spanned a wide spectrum of hardware types from small, distributed microphone arrays to large microphone arrays including 64 microphones. For two of the DCs working on spatial sound, a crucial step has been the design of simulation environments tailored for bioacoustics where spatial algorithms could be prototyped. We have also investigated the ecological footprint of bioacoustic hardware, eliciting requirements from practitioner interviews and making initial designs for low-footprint strategies which will be further developed and evaluated in the second half of the project.
The computational methods used in Bioacoustic AI come under two main categories: signal processing, and AI. A collaboration among all of the DCs resulted in the first contribution of a technical report, which gives an overview of on-device signal processing and artificial intelligence (AI) algorithms used in bioacoustics.
On the AI/machine learning side specifically, one focus is to move beyond basic classification, to tasks that suit the special constraints of wildlife monitoring tasks. To this end, initial results from various ML strategies have been developed, in three separate PhD projects. One uses human-in-the-loop “active learning”; one explores feature representations from pre-trained deep “embeddings” to facilitate discovery of rare sounds; and one has investigated how to use multiple datasets together in “multi-task” AI. The first results from these have been published in research workshops. Software implementations have also been made public, in particular a software framework “bacpipe”
https://github.com/bioacoustic-ai/bacpipe(odnośnik otworzy się w nowym oknie) to provide the core task of deep feature extraction from sounds.
Animal sound datasets come from multiple sources. There are pre-existing datasets, though highly variable in their size and the detail of their metadata. To assist with navigating and curating these, we built an online web directory “Bioacoustics Datasets”
https://bioacoustic-ai.github.io/bioacoustics-datasets/(odnośnik otworzy się w nowym oknie) which lists over 90 existing datasets representing many different types of animals. Additionally, we have curated data into new subsets, and/or add new manual annotations of phenomena. All of these are progressing satisfactorily. Open data publication will largely occur at the same time as the publication of the research papers describing them.
Animal behaviour is the focus of two DCs, using audio to characterise animal interactions and social information sharing. For the work on hyenas, existing audio data has been annotated and curated, and a recent AI model “animal2vec” has been customised. For the work on birds (yellowhammer), analysis of pre-collected datasets has revealed social effects of distance. Fieldwork has also been conducted to perform sound transmission experiments.
Ecological monitoring will benefit from the increased quality of acoustic surveying we develop. For birds, DC9 conducted sound transmission experiments in the field to measure how the identifiability of individual song varies with distance, and used this to design and then deploy Audiomoth recorders in the main fieldwork carried out in 2025 from March to July. For bats, DC10 curated data and trained an initial AI model for bat call detection, and also performed a wide range of community coordination/facilitation activities to build capacity and collaborations for large-scale bat recognition algorithms.
