Lightweight Hopfield Neural Networks for Bioacoustic Detection and Call Monitoring of Captive Primates

2025-11-04

AI Summary

Passive acoustic monitoring faces challenges of data processing latency, high annotation demands, and excessive computational costs associated with conventional CNN-based models. Method: This study proposes a lightweight, interpretable, and rapidly trainable Hopfield Neural Network (HNN) framework—the first application of HNNs to primate vocalization classification—specifically targeting social calls of captive black-and-white ruffed lemurs. The model integrates bat-inspired echolocation-motivated signal preprocessing and motion-correlated feature enhancement, enabling effective learning from minimal labeled samples. Training and real-time inference are achieved in milliseconds on standard laptop hardware. Contribution/Results: The framework achieves an overall accuracy of 0.94, a throughput of 340 frames per second, and processes over 5.5 hours of audio per minute. It drastically shortens the “data-to-decision” cycle, establishing a novel low-resource paradigm for bioacoustic monitoring in both field and captive settings.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Passive acoustic monitoring is a sustainable method of monitoring wildlife and environments that leads to the generation of large datasets and, currently, a processing backlog. Academic research into automating this process is focused on the application of resource intensive convolutional neural networks which require large pre-labelled datasets for training and lack flexibility in application. We present a viable alternative relevant in both wild and captive settings; a transparent, lightweight and fast-to-train associative memory AI model with Hopfield neural network (HNN) architecture. Adapted from a model developed to detect bat echolocation calls, this model monitors captive endangered black-and-white ruffed lemur Varecia variegata vocalisations. Lemur social calls of interest when monitoring welfare are stored in the HNN in order to detect other call instances across the larger acoustic dataset. We make significant model improvements by storing an additional signal caused by movement and achieve an overall accuracy of 0.94. The model can perform $340$ classifications per second, processing over 5.5 hours of audio data per minute, on a standard laptop running other applications. It has broad applicability and trains in milliseconds. Our lightweight solution reduces data-to-insight turnaround times and can accelerate decision making in both captive and wild settings.

Problem

• Automating bioacoustic detection to process large wildlife monitoring datasets efficiently
• Replacing resource-intensive neural networks with lightweight associative memory models
• Enabling rapid vocalization analysis for primate welfare monitoring in captive settings

Innovation

• Lightweight Hopfield Neural Networks for bioacoustic detection
• Associative memory AI model with transparent fast training
• Movement signal storage achieving 0.94 accuracy classification