並列摘要
Crying is the way that infants express their needs to their parents. Many baby products had provided the function of crying detection and recognition nowadays. In order to analyze the audio collected from the baby products, it is very critical to remove the noise that affects the result of the analysis. In this paper, we compared the capability of crying detection under different classifiers. A total of 58 acoustic features were extracted, then the Sequential Forward Selection algorithm and Fisher’s Discriminant Ratio are adopted to figure out the best feature set matched for each classifiers. Three classifiers, including Support Vector Machine, Random Forest and CART-Adaboost were used to compare the performance. The experiments show that the recognition accuracy of SVM, Random Forest, and AdaBoost are 92%, 91%, and 89%, respectively.