Linear prediction coding analysis and self-organizing feature map as tools to classify stress calls of domestic pigs (Sus scrofa).

Abstract

It is assumed that calls may give information about the inner (emotional) state of an animal. Hence, in the last years sound analysis has become an increasingly important tool for the interpretation of the behavior, the health condition, and the well-being of animals. A procedure was developed that allows the characterization, classification, and visualization of the cluster structures of stress calls of domestic pigs (Sus scrofa). Based on the acoustic model of the sound production the extraction of features from calls was performed with linear prediction coding (LPC). A vector-based self-organizing neuronal network was trained with the determined LPC coefficients, resulting in a feature map. The cluster structure of the calls was then visualized with a unified matrix and the neurons were labeled for their input origin. The basic applicability of the procedure was tested by using two examples which were of special interest for a possible evaluation of the normal farming practice. The procedure worked well both in discriminating individual piglets by their scream characteristics and in classifying pig stress calls vs other calls and noise occurring under normal farming conditions.