Intelligent farming as part of the green revolution is advancing the world of agriculture in such a way that farms become evolving, with the scope being the optimization of animal production in an eco-friendly way. In this direction, we propose exploiting the acoustic modality for farm monitoring. Such infor- mation could be used in a stand-alone or complimentary mode to monitor constantly animal population and behavior. To this end, we designed a scheme classifying the vocalizations produced by farm animals. More precisely, we propose a directed acyclic graph, where each node carries out a binary classi?cation task using hidden Markov models. The topological ordering follows a criterion derived from the Kullback-Leibler divergence. During the experimental phase, we employed a publicly available dataset including vocalizations of seven animals typically encountered in farms, where we report promising recognition rates outperform- ing state of the art classifiers.
A Classification Scheme Based on Directed Acyclic Graphs for Acoustic Farm Monitoring / S. Ntalampiras - In: FRUCT'23 : Proceedings / [a cura di] S. Balandin, T.S. Cinotti, F. Viola, T. Tyutina. - [s.l] : ACM, 2018. - pp. 276-282 (( Intervento presentato al 23. convegno Conference of Open Innovations Association FRUCT tenutosi a Bologna nel 2018.
A Classification Scheme Based on Directed Acyclic Graphs for Acoustic Farm Monitoring
S. Ntalampiras
2018
Abstract
Intelligent farming as part of the green revolution is advancing the world of agriculture in such a way that farms become evolving, with the scope being the optimization of animal production in an eco-friendly way. In this direction, we propose exploiting the acoustic modality for farm monitoring. Such infor- mation could be used in a stand-alone or complimentary mode to monitor constantly animal population and behavior. To this end, we designed a scheme classifying the vocalizations produced by farm animals. More precisely, we propose a directed acyclic graph, where each node carries out a binary classi?cation task using hidden Markov models. The topological ordering follows a criterion derived from the Kullback-Leibler divergence. During the experimental phase, we employed a publicly available dataset including vocalizations of seven animals typically encountered in farms, where we report promising recognition rates outperform- ing state of the art classifiers.File | Dimensione | Formato | |
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