A Classification and Recognition of Baby Crying

Understanding the need of the infant from his/her cry is a major topic which is studied by psychologists and medical doctors at the beginning. After the technological improvement, with the help of data analysis and data processing, this topic has got a big interest and be considered in many researches. In this project, we developed an approach to find a good accuracy of recognizing and classifying a newborn’s cry into 8 categories which are ‘Hunger, Need of Burping, Belly Pain, Discomfort, Tiredness, Loneliness, Cold/Hot and Scared’ . When we resolved the dataset problems, we extracted the MFCC features and according to that we run a kNN algorithm in the classification part. We obtained a good accuracy in this process which is 86% accuracy on testing by k=9 for the k-Value in kNN algorithm.