Acoustic classification of individual cat vocalizations in evolving environments

Abstract

This paper is focused on the classification of vocalizations characterizing intents of individual cats. Cats vocalize in order to convey different emotions and/or intents and although their repertoire/vocabulary may not be universal, it exhibits consistent characteristics on an individual basis. In this work, we present a complete pipeline for processing streams of audio, with the twofold goal being both detection as well as interpretation of cat vocalizations. The proposed system is based on YAMNet pre-trained deep network where we apply meaningful modifications addressing the requirements of the task-athand. Interestingly, the overall system is able to run in real-time on modern smartphones using the developed application. At the same time, we address the non-stationarity problem meaning that class dictionary is updated on-the-fly following user recommendations. After extensive experiments, we show that the proposed pipeline achieves quite satisfactory detection and recognition accuracy. To the best of our knowledge, this is the first attempt in the related literature to address continuous detection and interpretation of cat vocalizations in a real-time fashion.

Passive acoustic monitoring of animal populations with transfer learning

“…The training time did not significantly increase as a result. Ntalampiras et al (2021) created a classifier for cat vocalisations and consequently created a mobile application. The authors used YAMNet 1 pre-trained on the AudioSet-YouTube corpus (Gemmeke et al, 2017) which is an audio event classification dataset containing 512 classes.…”