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 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 (3 milliseconds), as only one representative signal is required for each target sound within a dataset. The model is fast, taking only 5.4 seconds 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 144.09 MB 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. • Introduces a first-of-its-kind associative memory AI model for bioacoustic detection. • Addresses the challenge of the timely analysis of the vast datasets generated by PAM. • Prototype AI model is fast, lightweight, sustainable, transparent and explainable. • Can be built and deployed on a standard laptop due to the low resource requirements. • Precision of up to 0.86 and F1 score of 0.81 despite dataset limitations.