Japanese Black Cattle Call Patterns Classification Using Multiple Acoustic Features and Machine Learning Models

Cattle calls have been used as indicators of the health and welfare of cattle, but the analysis of calls is laborious and time-consuming. In this study, we developed six machine learning classifiers to classify five cattle call patterns using the acoustic features of the calls. The classifiers were trained using calls collected from 31 Japanese Black cattle using a microphone and a webcam. The call patterns of interest included normal, estrus, feeding, normal calls with and without noise. We extracted 193 dimensions of acoustic features from each acoustic sample as the input for the call pattern classifiers. Two datasets were utilized to assess how background noise affected classification. Dataset2 combined two types of normal calls, whereas Dataset1 distinguished between normal calls with and without noise. The outcomes demonstrated that dataset-2 completed classification more accurately than dataset-1. K-nearest neighbor (95.62%), random forest (92.04%), decision tree (92.83%), and logistic regression (92.83%) all had high classification accuracy with dataset-2. When trained on dataset 2, Naive Bayes and support vector machine models' classification accuracy increased to 86.31% and 88.93%, respectively, from their initial values of 79.64% and 79.81% when using dataset-1. The similarity in call amplitudes and lengths resulted in incorrect classifications. In the future, a microphone with identity information could be attached near the cow's mouth to collect individual calls. In addition to calls associated with different physiological states, other types of information (e.g., sex, age, species, and individual acoustic characteristics), will be considered. This study offers a potential effective tool for precision livestock feeding and reproduction management based on cattle call patterns automatic recognition.