本文介绍了一种创新的AI模型，旨在解决生物声学监测中海量数据分析的难题。该模型针对现有AI模型在训练数据有限、环境影响大（尤其是在能源消耗和碳足迹方面）、以及硬件要求高等方面的局限性，提出了一种基于Hopfield神经网络的关联记忆方法。该模型训练速度快，内存占用小，计算需求低，适用于多种标准个人设备，甚至可以在边缘处理设备上部署。实验表明，该模型在蝙蝠回声定位声信号分类上的准确率高达86%，且没有发现与专家手动识别不一致的情况。该模型具有快速、轻量级、可持续、透明、可解释和准确的特点，有望改变生物声学分析的格局。

🧠 该模型的核心是基于Hopfield神经网络的关联记忆，通过存储和检测相似信号来进行物种分类。这种方法仅需每个目标声音的代表性信号进行训练，训练速度极快，仅需3毫秒。

⚡️ 该模型运行速度快，对10384个公开的蝙蝠录音进行预处理和分类仅需5.4秒，在标准Apple MacBook Air上完成。同时，内存占用小，仅需144.09MB的RAM。

💡 该模型对计算资源的需求较低，使其非常适合在各种标准个人设备上使用，甚至可以通过边缘处理设备在实地部署。这使得研究人员能够在资源有限的环境中进行高效的数据分析。

✅ 该模型的准确性具有竞争力，在用于评估的数据集上，准确率高达86%。在实验中，模型与专家手动识别的结果完全一致，没有出现任何差异。

arXiv:2507.10642v1 Announce Type: cross Abstract: A growing issue within conservation bioacoustics is the 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; environmental impact, particularly in energy consumption and carbon footprint of training and implementing these models; and associated hardware requirements. The model developed in this work uses associative memory via a transparent, explainable Hopfield neural network to store signals and detect similar signals which can then be used to classify species. Training is rapid ($3$\,ms), as only one representative signal is required for each target sound within a dataset. The model is fast, taking only $5.4$\,s to pre-process and classify all $10384$ publicly available bat recordings, on a standard Apple MacBook Air. The model is also lightweight with 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 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 demo this first-of-its-kind AI model, trained on only two representative 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.