“Virtual and Real Coexisting”: Zither Composition Practice and Aesthetic Reconstruction Through AI and VR Integration
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Abstract
This study presents a multi-modal signal processing framework for automatic guzheng composition and VR-based performance simulation. The main melody is extracted using a similarity matrix, while LSTM-DQN networks, combined with quantized reward mechanisms based on musical theory and playing techniques, generate harmonic sequences. Multi-channel sensory data—including audio, tactile feedback, hand gesture, and line-of-sight tracking—are processed and integrated to construct an interactive VR performance environment, forming a closed-loop feedback system analogous to multi-channel signal transmission and control in engineering. Experimental results demonstrate that the LSTM-DQN model achieves an average note prediction accuracy of 67.6% (σ=2.34%) and maintains high rhythmic fidelity, while user evaluations indicate satisfactory aesthetic quality (average score 3.04/5). The framework enables rapid transformation from compositional ideas to virtual performance outputs, highlighting the effectiveness of multisensor signal integration, temporal sequence learning, and real-time feedback in complex systems. This approach provides an engineering-oriented perspective for integrating AI-driven generative models with interactive feedback, supporting future applications in intelligent multi-channel signal processing and virtual performance control systems.
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References
F. Kouwenhoven, “China: The Guqin zither,” The other classical musics, pp. 105-125, 2015, doi: 10.1515/9781782045359-008.
Z. Li, “Aesthetic Origin of the Metal and Stone Sound in the Chinese Seven-String Zither Music,” Advances in Education, Humanities and Social Science Research, vol. 1, no. 1, pp. 53-53, 2022, doi: 10.56028/aehssr.1.1.53.
I. Zinkiv and J. Ren, “CHINESE ZITHER SE IN ANCIENT AND TRADITIONAL MUSICAL AND INSTRUMENTAL CULTURES OF TURKISH, MONGOLIAN AND TUNGUSKA-MANCHU ETHNIC GROUPS,” Yegah Müzikoloji Dergisi, vol. 7, no. 4, pp. 510-533, 2024, doi: 10.51576/ymd.1543588.
X. Hu, “Technology in the Combination of Traditional and Modern Music Composition,” In New Paradigm in Digital Classroom and Smart Learning: Proceedings of the International Conference on Digital Classroom and Smart Learning. New York, NY, USA: Springer Nature, vol. 54, pp. 294, 2024, doi: 10.1007/978-3-031-98607-9_29.
J. Zheng, M. Cao, and C. Zhang, “AI-driven generation of guzheng music from classical Chinese poetry: toward a new paradigm of creative practice in Chinese traditional Music,” Multimedia Systems, vol. 31, no. 6, pp. 437, 2025, doi: 10.1007/s00530-025-02023-w.
S. R. Marshall, T. N. D. Tran, M. R. Tapas, and B. Q. Nguyen, “Integrating artificial intelligence and machine learning in hydrological modeling for sustainable resource management,” International Journal of River Basin Management, pp. 1-17, 2025, doi: 10.1080/15715124.2025.2478280.
A. Taweesan, T. Kanabkaew, N. Surinkul, and C. Polprasert, “Integrating clustering algorithms and machine learning to optimize regional snapshot municipal solid waste management for achieving sustainable development goals,” Environmental Advances, vol. 19, Art. no. 100607, 2025, doi: 10.1016/j.envadv.2024.100607.
C. Wang, W. Xiong, and G. Zhang, “Application of deep learning models with spectral data augmentation and denoising for predicting total phosphorus concentration in water pollution,” Journal of the Taiwan Institute of Chemical Engineers, vol. 167, Art. no. 105852, 2025, doi: 10.1016/j.jtice.2024.105852.
W. Hussain, M. F. Mushtaq, M. Shahroz, U. Akram, E. S. Ghith, M. Tlija, et al., “Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning,” Scientific Reports, vol. 15, no. 1, pp. 1003, 2025, doi: 10.1038/s41598-024-76178-3.
X. Zhu, “RNN Language Processing Model-Driven Spoken Dialogue System Modeling Method,” Computational Intelligence and Neuroscience, vol. (1), Art. no. 6993515, 2022, doi: 10.1155/2022/6993515.
Y. Yang, “Performance skills and artistic conception of Chinese Zither music,” Frontiers in Art Research, pp. 3(3), 2021, doi: 10.25236/FAR.2021.030304.
Y. Kang, “Performance Techniques and Insights of the Guzheng Concerto "Chan Ge",” Journal of Education and Educational Research, vol. 9, no. 1, pp. 44-46, 2024, doi: 10.54097/m531pr78.
Q. Su, “Contemporary Guzheng Inheritance Issues and Strategies,” Arts, Culture and Language, pp. 1(10), 2024, doi: 10.61173/769vth96.
Y. Kang and A. Sh, “An Initial Exploration of the Historical Development and Origin of the Guzheng,” Highlights in Art and Design, vol. 6, no. 1, pp. 25-31, 2024, doi: 10.54097/gqgpm010.
Y. Zhang, X. Lv, Q. Li, X. Wu, Y. Su, and H. Yang, “An automatic music generation method based on RSCLN_Transformer network,” Multimedia systems, vol. 30, no. 1, pp. 4, 2024, doi: 10.1007/s00530-023-01245-0.