Automated Baby Cry Classification on a Hospital-acquired Baby Cry Database

Abstract

Timely addressing baby cries is always a challenge for new parents. Our project aims to develop a baby cry recognition system, capable of distinguishing between different kinds of baby cries, in real-world conditions. This will inform parents of their specific baby need, while they learn to make the distinction for themselves. In this study, we describe a series of experiments designed to establish the accuracy of popular machine learning algorithms on the categorization of 7 types of baby cries. We tested the algorithms on our own baby cry database, SPLANN[1], containing over 13K baby cries, recorded in a neonatal hospital. We extract acoustic features, perform best feature selection and report increased classification accuracies, from a coin-toss rate of 14.2%.