Improving the Interactive Accuracy of Intelligent Piano Teaching Using a PSO-SVM Optimized Fingering Recognition Model
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Abstract
Current intelligent piano teaching systems are limited by insufficient feature extraction and poorly optimized classification parameters, which reduces fingering-recognition accuracy and weakens human–machine interaction feedback. This paper proposes a high-precision fingering recognition method based on Particle Swarm Optimization and Support Vector Machine. Keystroke timing, force, and duration are first collected and normalized as discriminative feature vectors. A standard Particle Swarm Optimization algorithm is then designed with cross-validation accuracy as the fitness function to iteratively optimize the SVM kernel parameter γ and penalty factor C. The optimized classifier is trained on fingering samples and embedded into an intelligent piano teaching system for real-time recognition. Experiments on the PIG dataset, including 13,800 annotated fingering samples from ten trained pianists performing six graded piano pieces on a Yamaha U1 acoustic piano, show 91.2% recognition accuracy for two-finger fingering and an 87.0% F1-score for inter-finger repeated fingering. With an average response time below 67 ms, the system demonstrates high recognition accuracy and real-time interaction capability, providing a feasible technical path for intelligent piano instruction and short-duration motion-signal classification.
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References
H. Liu, “Quantitative Analysis and AI Assessment of Piano Performance Techniques,” Applied Mathematics and Nonlinear Sciences, vol. 10, no. 1, pp. 1-16, 2025, doi: 10.2478/amns-2025-0231.
H. Pan and W. Wu, “Online learning to play the piano: perspectives and achievements/Aprender a tocar el piano en línea: perspectivas y logros,” Culture and Education, vol. 36, no. 2, pp. 370-393, 2024, doi: 10.1177/11356405241259599.
Y. Wang, “Research on Artificial Intelligence and Piano Playing Technology in Colleges and Universities Based on 5g Big Data Networks,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, pp. 1-16, 2024, doi: 10.2478/amns-2024-1881.
V. Phanichraksaphong and W. H. Tsai, “Automatic evaluation of piano performances for STEAM education,” Applied Sciences, vol. 11, no. 24, pp. 11783-11805, 2021, doi: 10.3390/app112411783.
X. Guan, H. Zhao, and Q. Li, “Estimation of playable piano fingering by pitch-difference fingering match model,” EURASIP Journal on Audio, Speech, and Music Processing, vol. 2022, no. 1, pp. 7-20, 2022, doi: 10.1186/s13636-022-00237-8.
T. Oku and S. Furuya, “Noncontact and high-precision sensing system for piano keys identified fingerprints of virtuosity,” Sensors, vol. 22, no. 13, pp. 4891-4902, 2022, doi: 10.3390/s22134891.
N. Srivatsan and T. Berg-Kirkpatrick, “Checklist models for improved output fluency in piano fingering prediction,” arXiv preprint arXiv, vol. 22, no. 09, Art. no. 05622, 2022, doi: 10.48550/arXiv.2209.05622.
J. Ye, “Research on the application of vocal music teaching methods and techniques in colleges and universities based on proportional integral differentiation algorithm,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, pp. 1–21, 2024, doi: 10.2478/amns-2024-1327.
S. K. Prabhakar and D. O. Won, “Efficient strategies for finger movement classification using surface electromyogram signals,” Frontiers in neuroscience, vol. 17, Art. no. 1168112, 2023, doi: 10.3389/fnins.2023.1168112.
Q. Gan, S. Wang, S. Wu, and J. Zhu, “Pianomotion10m: Dataset and benchmark for hand motion generation in piano performance,” arXiv preprint arXiv, vol. 2406, Art. no. 09326, 2024, doi: 10.48550/arXiv.2406.09326.