Automatic detection of cow/calf vocalizations in free-stall barn

Precision livestock farming dictates the use of advanced technologies to understand, analyze, assess and finally optimize a farm\u2019s production collectively as well as the contribution of each single animal. This work is part of a research project wishing to steer the dairy farms\u2019 producers to more ethical rearing systems. To study cow\u2019s welfare, we focus on reciprocal vocalizations including mother-offspring contact calls. We show the set-up of a suitable audio capturing system composed of automated recording units and propose an algorithm to automatically detect cow vocalizations in an indoor farm setting. More specifically, the algorithm has a two-level structure: a) first, the Hilbert follower is applied to segment the raw audio signals, and b) second the detected blocks of acoustic activity are refined via a classification scheme based on hidden Markov models. After thorough evaluation, we demonstrate excellent detection results in terms of false positives, false negatives and confusion matrix

A Classification Scheme Based on Directed Acyclic Graphs for Acoustic Farm Monitoring

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 classification 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

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