Data Classification Using BP Neural Networks

In this article, we explore a common signal classification problem: speech feature signal classification. Speech signals represent a crucial signal type as they convey both linguistic and emotional information. The process of speech signal classification involves categorizing speech signals into distinct classes, with applications spanning numerous fields such as natural language processing, audio signal processing, and audio retrieval systems. This article introduces the fundamental principles and commonly used techniques for speech feature signal classification, including the problem background, practical applications, methodological approaches, and relevant technologies. We will demonstrate key implementation aspects through practical examples, such as feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs) and classification through Backpropagation Neural Networks with typical architectures containing input, hidden, and output layers. The discussion includes algorithm explanations for gradient descent optimization and activation functions like sigmoid or ReLU, helping readers better understand the complete classification pipeline from raw audio preprocessing to final classification results.