  Animal vocalisations contain important information about health, emotional
state, and behaviour, thus can be potentially used for animal welfare
monitoring. Motivated by the spectro-temporal patterns of chick calls in the
time$-$frequency domain, in this paper we propose an automatic system for chick
call recognition using the joint time$-$frequency scattering transform (JTFS).
Taking full-length recordings as input, the system first extracts chick call
candidates by an onset detector and silence removal. After computing their JTFS
features, a support vector machine classifier groups each candidate into
different chick call types. Evaluating on a dataset comprising 3013 chick calls
collected in laboratory conditions, the proposed recognition system using the
JTFS features improves the frame- and event-based macro F-measures by 9.5% and
11.7%, respectively, than that of a mel-frequency cepstral coefficients
baseline.
