Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm - ZhuParris, Ahnjili; Kruizinga, Matthijs D.; Gent, Max van; Dessing, Eva; Exadaktylos, Vasileios; Doll, Robert Jan; Stuurman, Frederik E.; Driessen, Gertjan A.; Cohen, Adam F. (Frontiers in Pediatrics, 2021)
Introduction: The duration and frequency of crying of an infant can be indicative of its health. Manual tracking and labelling of crying is laborious, subjective, and sometimes inaccurate. The aim of this study was to develop and technically validate a smartphone-based algorithm able to automatically detect crying. Methods: For the development of the algorithm a training dataset containing 897 5-second clips of crying infants and 1263 clips of non-crying infants and common domestic sounds was assembled from various online sources. OpenSMILE software was used to extract 1591 audio features per audio clip. A random forest classifying algorithm was fitted to identify crying from non-crying in each audio clip. For the validation of the algorithm, an independent dataset consisting of real-life recordings of 15 infants was used. A 29-minute audio clip was analyzed repeatedly and under differing circumstances to determine the intra- and inter- device repeatability and robustness of the algorithm. Results: The algorithm obtained an accuracy of 94% in the training dataset and 99% in the validation dataset. The sensitivity in the validation dataset was 83%, with a specificity of 99% and a positive- and negative predictive value of 75% and 100%, respectively. Reliability of the algorithm appeared to be robust within- and across devices, and the performance was robust to distance from the sound source and barriers between the sound source and the microphone. Conclusion: The algorithm was accurate in detecting both cry duration and cry sessions and was robust to various changes in ambient settings.