System for audio capture and classification of baby cry samples


We explore multiclass classification of infants' cries and the relation between the age of the infant and the accuracy of classification. Additionally we explore secure cloud storage and cloud data processing. We compare several state-of-the-art multiclass classification models with recurrent neural networks. Classification accuracy was obtained on data from infants of various ages. For data storage and processing we used the Django Rest API and the opensource cloud platform OpenStack. Multiclass classification models successfully differentiated between different classes of crying, but no age effect has been found. We have demonstrated the aptness of the Django Rest API and OpenStack platform for data storing and processing in the cloud

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This paper was published in ePrints.FRI.

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