There is a critical need to develop and validate non-invasive animal-based
indicators of affective states in livestock species, in order to integrate them
into on-farm assessment protocols, potentially via the use of precision
livestock farming (PLF) tools. One such promising approach is the use of vocal
indicators. The acoustic structure of vocalizations and their functions were
extensively studied in important livestock species, such as pigs, horses,
poultry and goats, yet cattle remain understudied in this context to date. Cows
were shown to produce two types vocalizations: low-frequency calls (LF),
produced with the mouth closed, or partially closed, for close distance
contacts and open mouth emitted high-frequency calls (HF), produced for long
distance communication, with the latter considered to be largely associated
with negative affective states. Moreover, cattle vocalizations were shown to
contain information on individuality across a wide range of contexts, both
negative and positive. Nowadays, dairy cows are facing a series of negative
challenges and stressors in a typical production cycle, making vocalizations
during negative affective states of special interest for research. One
contribution of this study is providing the largest to date pre-processed
(clean from noises) dataset of lactating adult multiparous dairy cows during
negative affective states induced by visual isolation challenges. Here we
present two computational frameworks - deep learning based and explainable
machine learning based, to classify high and low-frequency cattle calls, and
individual cow voice recognition. Our models in these two frameworks reached
87.2% and 89.4% accuracy for LF and HF classification, with 68.9% and 72.5%
accuracy rates for the cow individual identification, respectively.

通过使用计算框架，本研究提供了从视觉隔离挑战中诱导的泌乳成年多胎生奶牛在负面情感状态下的最大程度预处理数据集（不含噪音），以实现低频和高频牛叫声的分类以及个体奶牛语音识别。其中，深度学习和可解释的机器学习框架的模型，分别达到了 87.2% 和 89.4% 的低频和高频分类准确率，以及 68.9% 和 72.5% 的个体牛辨识准确率。