Feature Classification of Speech Signals Using BP Neural Network

In this study, we select folk music, guzheng (Chinese zither), rock, and pop music as classification targets and implement their effective classification using a Backpropagation (BP) neural network. Let us explore these music genres in greater detail with their technical implementation aspects.

First, folk music. Folk music represents musical forms originating from cultural traditions, conveying emotions and stories through simple melodies and lyrics. In the BP network implementation, we typically extract acoustic features such as Mel-Frequency Cepstral Coefficients (MFCCs) and spectral characteristics as input vectors. The network architecture might consist of an input layer matching the feature dimension, hidden layers with sigmoid activation functions, and an output layer using softmax for multi-class classification of folk music subtypes like folk love songs and folk dance music.

Next, guzheng music. The guzheng is a traditional Chinese instrument known for its resonant timbre and distinctive playing techniques, primarily used for classical and traditional repertoire. For classification, we implement pre-processing algorithms to capture unique instrumental characteristics, including temporal features like attack-decay patterns and frequency-domain features specific to string instruments. The BP network's learning algorithm adjusts weights through gradient descent to minimize classification error between guzheng subgenres such as classical guzheng pieces and folk guzheng compositions.

Then, rock music. Rock music embodies high-energy musical expression characterized by electric guitars, drums, and bass instruments, featuring strong rhythms and guitar solos. Our implementation involves extracting high-energy frequency components and rhythmic patterns through digital signal processing. The BP network configuration may incorporate momentum-based optimization to handle the dynamic range of rock subgenres like classic rock and hard rock, with cross-validation techniques ensuring model generalization.

Finally, pop music. As one of the most contemporary popular genres, pop music integrates diverse styles and elements with broad audience appeal. The classification system employs feature normalization to handle stylistic variations, while the BP network utilizes backpropagation algorithms with regularization to prevent overfitting when distinguishing between pop subcategories such as dance pop and pop rock.

Through this analysis, we demonstrate that BP network classification of folk, guzheng, rock, and pop music proves highly effective and meaningful. This approach facilitates deeper understanding and appreciation of diverse music genres while providing enhanced services and recommendations for music enthusiasts through automated pattern recognition systems.