Design of Emotion Analysis Model IABC-Deep Learning-based for Vocal Performance

With the development of deep learning technology, and due to its potential in solving optimization problems with deep structures, deep learning technology is gradually being applied to sentiment analysis models. However, most existing deep learning-based sentiment analysis models have low accuracy issues. Therefore, this study focuses on the issue of emotional analysis in vocal performance. Firstly, based on vocal performance experts and user experience, classify the emotions expressed in vocal performance works to identify the emotional representations of music. On this basis, in order to improve the accuracy of emotion analysis models for deep learning based vocal performance, an improved artificial bee colony algorithm (IABC) was developed to optimize deep neural networks (DNN). Finally, the effectiveness of the proposed deep neural network based on improved artificial bee colony (IABC-DNN) was verified through a training set consisting of 150 vocal performance works and a testing set consisting of 30 vocal performance works. The results indicate that the accuracy of the sentiment analysis model for vocal performance based on IABC-DNN can reach 98%.

@article{Zhu2024,
  title     = {Design of Emotion Analysis Model IABC-Deep Learning-based for Vocal Performance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Zhenjie Zhu and Xiaojie Lv},
  doi       = {10.14569/IJACSA.2024.0150480},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150480}
}