Pig Voice Classification Algorithm Based on Deep Neural Network

This paper proposes a method that integrates WaveNet, Deep Belief Network (DBN), and Variational Autoencoder. The new pig voice classification algorithm of VAE - Variational Belief Encoder Network (VBEN). This algorithm is designed to improve the accuracy of pig sound classification. Firstly, WaveNet is used to capture the fine-grained features in pig sound signals and generate high-quality sound representations. Then, the Deep Belief Network (DBN) conducts multi-level abstraction and extraction of sound features to enhance the model's learning ability for complex sound patterns. Finally, the variational autoencoder (VAE) encodes and decodes the sound features by introducing a probabilistic generative model, further optimizing the feature representation and enhancing the generalization ability of the model. The experimental results show that VBEN performs well in the task of classifying pig sounds. Compared with traditional methods, it can identify different types of pig sounds more accurately, providing strong support for health management in pig farming. This method effectively improves the stability and adaptability of pig sound classification, providing feasible technical support for animal behavior analysis and health monitoring in intelligent breeding.