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 black-and-white ruffed lemur


Rare twin lemurs born at New York's Central Park Zoo

Popular Science

Environment Conservation Land Rare twin lemurs born at New York's Central Park Zoo The new Critically Endangered black-and-white ruffed lemurs are particularly fond of bananas. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Twin black-and-white ruffed lemurs, Maso and Tonga, explore their habitat at Central Park Zoo with their mother, Koa. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .


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

arXiv.org Artificial Intelligence

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.