Machine Learning Approach for Infant Cry Interpretation

Infant's cry is an innate response to express several situations including pain, disturbance and discomfort. Therefore the automatic recognition of infant cries patterns is the key factor to develop successful ambient intelligence applications to enhance the quality of life of both infants and parents. This paper proposes a complete machine learning process including consistent dataset generation from infant cries and selecting appropriate sound features, with promising experimental results for enhancing the monitoring of infants in real world settings. The originality of the proposed approach lies in its ability to detect and analyze automatically discomfort signals, which recurrently affects 20 to 25% of newborns. The machine learning process includes low-level audio features selection methods from labeled infant pre-cry recordings as well as high-level features characterizing the envelop of the crying. The classification is performed using ensemble learning methods after a stage of features selection. The exploitation of pre-crying signals to improve the quality of the recognition is another important aspect of the proposed approach, which optimizes the accuracy of the learning step as it is shown by the obtained results on a real dataset. This result gives the opportunity to develop new baby monitors able to anticipate the infants needs.