首页|Studies from Goethe-University Frankfurt Update Current Data on Artificial Intel ligence (Acoustic estimation of the manatee population and classification of cal l categories using artificial intelligence)
Studies from Goethe-University Frankfurt Update Current Data on Artificial Intel ligence (Acoustic estimation of the manatee population and classification of cal l categories using artificial intelligence)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting originating from Frankfurt am Mai n, Germany, by NewsRx correspondents, research stated, "The population sizes of manatees in many regions remain largely unknown, primarily due to the challengin g nature of conducting visual counts in turbid and inaccessible aquatic environm ents. Passive acoustic monitoring has shown promise for monitoring manatees in t he wild." Our news editors obtained a quote from the research from Goethe-University Frank furt: "In this study, we present an innovative approach that leverages a convolu tional neural network (CNN) for the detection, isolation and classification of m anatee vocalizations from long-term audio recordings. To improve the effectivene ss of manatee call detection and classification, the CNN works in two phases. Fi rst, a longterm audio recording is divided into smaller windows of 0.5 seconds and a binary decision is made as to whether or not it contains a manatee call. S ubsequently, these vocalizations are classified into distinct vocal classes (4 c ategories), allowing for the separation and analysis of signature calls (squeaks ). Signature calls are further subjected to clustering techniques to distinguish the recorded individuals and estimate the population size. The CNN was trained and validated using audio recordings from three different zoological facilities with varying numbers of manatees. Three different clustering methods (community detection with two different classifiers and HDBSCAN) were tested for their suit ability. The results demonstrate the ability of the CNN to accurately detect man atee vocalizations and effectively classify the different call categories. In ad dition, our study demonstrates the feasibility of reliable population size estim ation using HDBSCAN as clustering method."
Goethe-University FrankfurtFrankfurt a m MainGermanyEuropeArtificial IntelligenceEmerging TechnologiesMachine Learning
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