The Application of Computer Music Analysis Technique in the Characteristic Analysis of Vocal Music Works

In the current diversified music creation and consumption environment, the incubation of high-quality music is facing unprecedented challenges, partly due to the significant limitations of traditional beat tracking algorithms in dealing with complex and ever-changing music structures. Therefore, this paper innovatively proposes a real-time music beat tracking algorithm that integrates embedded neural network technology, aiming to break through technical bottlenecks and reshape the paradigm of music feature extraction and classification. The algorithm first conducted in-depth research on the feature extraction and classification technology model of music signals. By integrating embedded neural network technology, deep learning and precise capture of music features have been achieved, effectively overcoming the shortcomings of traditional methods in processing complex music features. On this basis, we further introduced embedded neural networks and utilized their powerful optimization search capabilities to intelligently adjust the data layout for music feature extraction and classification, thereby significantly improving the accuracy of feature extraction and the reliability of classification. To verify the effectiveness of the algorithm, we applied it to real-time detection of rhythm values and precise beat point positions in music. By comparing with international authoritative evaluation datasets such as MIREX2006, the significant improvement of this algorithm in time performance and accuracy was demonstrated.