Multi-Model Ensemble and Optimization for Baby Cry Analysis: Application of CNN and LSTM Methods

Baby crying is an important way for babies to express their needs and discomfort. Accurately analyzing their crying is of great significance to caregivers. This paper aims to solve the problem of baby crying analysis. It adopts a variety of data enhancement methods, uses LSTM and CNN deep learning models, and combines integrated ensemble and transfer learning methods to design and optimize the algorithm model. The experimental results show that the multi-model fusion and data enhancement methods effectively improve the accuracy and robustness of baby crying classification and analysis.