Automated newborn cry diagnostic system using machine learning approach

Researchers have found that crying is an acoustic symptom among unhealthy newborns. This study aims to develop a non-invasive newborn cry diagnostic system (NCDS) using information at different levels of the cry audio signal (CAS) of infants. The unhealthy newborn group in our experiment consists of 34 clinical cases. The proposed machine learning (ML) techniques include the extraction of feature sets of Mel frequency cepstral coefficients (MFCC), auditory-inspired amplitude modulation (AAM) features, and a prosody feature set of tilt, intensity, and rhythm features. The training models are probabilistic neural networks and support vector machine algorithms. The feature sets of AAM and MFCC extract low-level patterns, whereas the prosody feature set of tilt, intensity, and rhythm extracts high-level information in an infant CAS. The AAM feature set in the NCDS has never yet been examined. As an innovative aspect of this study, the AAM feature set is included in the NCDS, and this feature set is fused with the feature sets of MFCC and prosody. As another innovation, we reproduce realworld problems by including many pathologies in the unhealthy group. Among the evaluated frameworks proposed, the fusion of all feature sets improves the system performance. The best result is obtained with the fusion of AAM and MFCC with an F-measure of over 80%. The results of this experiment reveal the usefulness of information at different levels within the CASs of newborns, which vary among healthy and unhealthy groups. Moreover, to identify unhealthy newborns, this information can be captured noninvasively by applying ML methods to the NCDS.