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.
|Titolo:||A Classification Scheme Based on Directed Acyclic Graphs for Acoustic Farm Monitoring|
|Parole Chiave:||audio pattern recognition; audio signal processing; directed acyclic graph; intelligent farming; sound classification|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2018|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|