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

TL;DR

This paper introduces a lightweight, fast, and transparent Hopfield neural network model for bioacoustic detection of primate calls, significantly improving processing speed and flexibility over traditional deep learning methods.

Contribution

The authors develop a novel, resource-efficient Hopfield neural network model tailored for bioacoustic monitoring, enabling rapid training and high accuracy in call detection.

Findings

Achieved 94% accuracy in detecting lemur vocalizations.

Processed over 5.5 hours of audio per minute on a standard laptop.

Performed 340 classifications per second, enabling real-time analysis.

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.