Acoustic sensors for automated detection of cow vocalization duration and type

•Cow attached audio sensors can discern between vocalization and background noise.•The accuracy of the vocalization classification model was 99.5%.•The model for the duration of vocalization had an R2 = 0.84.•Algorithms can predict open, closed, and mixed mouth vocalization (accuracy of 85%).•Classification models for voice recognition had an accuracy of 80%. Acoustic technologies provide a non-invasive method to generate information about cow vocalization. This study demonstrated that collar attached acoustic sensors can differentiate between cow vocalization and background sounds obtained from cows under grazing conditions. The overall accuracy of the vocalization classification model was 99.5% in the test dataset, based on a total of 709 vocalization recordings from 10 cows. Vocalization samples were obtained from 10 trial cows, with a frequency that ranged from 3 to 452 vocalizations per cow. Algorithms were also developed to differentiate between three different cow vocalization classes (Open mouth, Closed mouth, Mixed mouth (Closed mouth followed by Open mouth)), with a model accuracy of 85% in the test dataset. Most cows demonstrated all three types of vocalization, and the between-cow variability in the probability of mixed vocalization per vocalization had a standard deviation of 0.13 relative an average probability of 0.74. The duration of the 709 individual vocalizations ranged from 0.88 to 3.37 s, with an average of 1.76 s and a standard deviation of 0.36 s. There was a between-cow variation in the duration of vocalization, with a standard deviation of 0.12 ± 0.04 s (P