Automatic recognition of rodent call types using deep supervectors

Abstract

Rats are gregarious rodents who naturally live in diverse social groups and communicate in part through ultrasonic vocalisations (USVs). USVs encode significant information about affective state and play an important role in social behaviour. Monitoring USVs is a non-invasive method of adding richness to data in a variety of experimental paradigms. However, manual analysis of USVs requires a significant amount of human effort. We propose a new method for automatic classification of USVs which could help automate analysis and thus reduce human input. The proposed method introduces a novel approach to USV representation called deep supervectors (DSV), which combines diverse deep embeddings feature sets extracted through our active data representation (ADR) method. The DSV method is evaluated on a multiclass recognition task involving 14 different types of rodent calls. The performance of DSV is compared to that obtained by state-of-the-art deep embeddings (alexNet, googleNet, squeezeNet and resNet). The proposed method achieves an Unweighted Average Recall (UAR) of 32.80% and outperforms both customs (8.07%) and deep embeddings (29.94%). Deep supervectors outperform pre-trained DNN in 6 out of 8 cases and fusion of the top six DSV with the top two deep embeddings improves the UAR to 37.22% in this challenging classification task, showing an improvement of 29% over the majority guess of 8%. By combining related classes, the UAR will reach 51.00%. The context of this application is the automation of the process for life-sciences laboratory technicians and scientists.