Classification of Infant Behavioural Traits using Acoustic Cry: An Empirical Study

Abstract

The reason behind an infant's cry has been elusive to sometimes even the most skilled and experienced paediatricians. Our comprehensive research aims to classify infant's cry into their behavioural traits using objective and analytical machine learning approaches. Towards this goal, we compare conventional machine learning and more recent deep learning-based models for baby cry classification, using acoustic features, spectrograms, and a combination of the two. We performed a detailed empirical study on the publicly available donateacry-corpus and the CRIED dataset to highlight the effectiveness of appropriate acoustic features, signal processing, or machine learning techniques for this task. We also conclude that acoustic features and spectrograms together bring better results. As a side result, this work also emphasized the challenge of an inadequate baby cry database in modelling infant behavioural traits.