CRY CLASSIFIACATION - Bhati, Drishti (Journal of Advanced Research in Signal Processing and Applications, 2021)
Infant cry classification is the collection of unstructured data. Unstructured data is the data which do not have any pre-defined structure such as audio, image and video. Dealing with audio is way more difficult, challenging and time consuming. Infant cry classification is use to identify the voice of child who is newly born. Cry of babies are so unpredicted that just with voice it is not possible to tell the reason behind their cries. It identify cry voice to detect the information regarding the state of an infant, such as hunger, pain or uncomfortable. Cries play an important role when children unable to speak, at that time their cry audio tells about their activity. In this paper technique which used to find the reason of cry classification is Short Time Fourier Transform (STFT). This research develops a system to classify the infants cry sound by performing feature extraction using MFCC (Mel-Frequency Cepstral Coefficients). The coefficients are then passed on to Back Propagation Neural Network (BNN) with 8 neurons in the hidden layer, for classification of the audio samples into three class namely-hungry, anger and fear. How to cite this article: Bhati D. Cry Classifiacation. J Adv Res Sig Proc Appl 2020; 2(2): 8-10