First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural network
A growing issue within conservation bioacoustics is the laborious task of analysing the vast amount of data generated from the use of passive acoustic monitoring devices. In this paper, we present an alternative AI model which has the potential to help alleviate this problem. Our model formulation a...
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Elsevier
2025-11-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003917 |
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| author | Andrew Gascoyne Wendy Lomas |
| author_facet | Andrew Gascoyne Wendy Lomas |
| author_sort | Andrew Gascoyne |
| collection | DOAJ |
| description | A growing issue within conservation bioacoustics is the laborious task of analysing the vast amount of data generated from the use of passive acoustic monitoring devices. In this paper, we present an alternative AI model which has the potential to help alleviate this problem. Our model formulation addresses the key issues encountered when using current AI models for bioacoustic analysis, namely: the limited training data available; the environmental impact, particularly in energy consumption and carbon footprint of training and implementing these models; and the associated hardware requirements. The model developed in this work uses associative memory via a transparent and explainable Hopfield neural network to store signals and detect similar signals which can then be used to classify species. Training is rapid (3milliseconds), as only one representative signal is required for each target sound within a dataset. The model is fast, taking only 5.4seconds to pre-process and classify all 10384 publicly available bat recordings, on a standard Apple MacBook Air. The model is also lightweight, i.e., has a small memory footprint of 144.09MB of RAM usage. Hence, the low computational demands make the model ideal for use on a variety of standard personal devices with potential for deployment in the field via edge-processing devices. It is also competitively accurate, with up to 86% precision on the labelled dataset used to evaluate the model. In fact, we could not find a single case of disagreement between model and manual identification via expert field guides. Although a dataset of bat echolocation calls was chosen to demonstrate this first-of-its-kind AI model, trained on only two representative echolocation calls, the model is not species specific. In conclusion, we propose an equitable AI model that has the potential to be a game changer for fast, lightweight, sustainable, transparent, explainable and accurate bioacoustic analysis. |
| format | Article |
| id | doaj-art-39d41cf9fbe34b859a59be4cae85892a |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-39d41cf9fbe34b859a59be4cae85892a2025-08-20T05:06:01ZengElsevierEcological Informatics1574-95412025-11-019110338210.1016/j.ecoinf.2025.103382First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural networkAndrew Gascoyne0Wendy Lomas1Corresponding author.; Faculty of Science and Engineering, University of Wolverhampton, Wulfruna Street, Wolverhampton, WV1 1LY, UKFaculty of Science and Engineering, University of Wolverhampton, Wulfruna Street, Wolverhampton, WV1 1LY, UKA growing issue within conservation bioacoustics is the laborious task of analysing the vast amount of data generated from the use of passive acoustic monitoring devices. In this paper, we present an alternative AI model which has the potential to help alleviate this problem. Our model formulation addresses the key issues encountered when using current AI models for bioacoustic analysis, namely: the limited training data available; the environmental impact, particularly in energy consumption and carbon footprint of training and implementing these models; and the associated hardware requirements. The model developed in this work uses associative memory via a transparent and explainable Hopfield neural network to store signals and detect similar signals which can then be used to classify species. Training is rapid (3milliseconds), as only one representative signal is required for each target sound within a dataset. The model is fast, taking only 5.4seconds to pre-process and classify all 10384 publicly available bat recordings, on a standard Apple MacBook Air. The model is also lightweight, i.e., has a small memory footprint of 144.09MB of RAM usage. Hence, the low computational demands make the model ideal for use on a variety of standard personal devices with potential for deployment in the field via edge-processing devices. It is also competitively accurate, with up to 86% precision on the labelled dataset used to evaluate the model. In fact, we could not find a single case of disagreement between model and manual identification via expert field guides. Although a dataset of bat echolocation calls was chosen to demonstrate this first-of-its-kind AI model, trained on only two representative echolocation calls, the model is not species specific. In conclusion, we propose an equitable AI model that has the potential to be a game changer for fast, lightweight, sustainable, transparent, explainable and accurate bioacoustic analysis.http://www.sciencedirect.com/science/article/pii/S1574954125003917BioacousticsArtificial intelligenceMachine learningHopfield neural networksSignal processing |
| spellingShingle | Andrew Gascoyne Wendy Lomas First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural network Ecological Informatics Bioacoustics Artificial intelligence Machine learning Hopfield neural networks Signal processing |
| title | First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural network |
| title_full | First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural network |
| title_fullStr | First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural network |
| title_full_unstemmed | First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural network |
| title_short | First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural network |
| title_sort | first of its kind ai model for bioacoustic detection using a lightweight associative memory hopfield neural network |
| topic | Bioacoustics Artificial intelligence Machine learning Hopfield neural networks Signal processing |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125003917 |
| work_keys_str_mv | AT andrewgascoyne firstofitskindaimodelforbioacousticdetectionusingalightweightassociativememoryhopfieldneuralnetwork AT wendylomas firstofitskindaimodelforbioacousticdetectionusingalightweightassociativememoryhopfieldneuralnetwork |