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<p>In the field of higher education in today’s era, cultivating vocal talents is crucial. This paper focuses on an innovative mechanism in vocal talent cultivation, i.e., enhancing vocal education by constructing a music recognition model based on Fourier algorithm. The Model is further applied to design a teaching aid system incorporating reinforcement learning to realize the synergistic development of digital media art and vocal talent cultivation. After rigorous testing, we found that the music recognition model has an accuracy rate of over 90% and is highly resistant to noise. In addition, by analyzing the practices of several colleges and universities’ music schools, it is observed that there are significant differences among different institutions in the four comparative items, with standard deviations exceeding 0.6. In particular, the average scores using this study’s teaching system for vocal talent cultivation are 4.312, 4.335, 4.267, and 4.354, respectively, generally higher than those of the other colleges. The mean values of these four different vocal music majors were all above 4 points. Therefore, the teaching system for vocal talent training proposed in this paper effectively improves the quality of vocal talent training and provides a new path for the practical reform and optimization of said education.</p>
</abstract>

In the field of higher education in today’s era, cultivating vocal talents is crucial. This paper focuses on an innovative mechanism in vocal talent cultivation, i.e., enhancing vocal education by constructing a music recognition model based on Fourier algorithm. The Model is further applied to design a teaching aid system incorporating reinforcement learning to realize the synergistic development of digital media art and vocal talent cultivation. After rigorous testing, we found that the music recognition model has an accuracy rate of over 90% and is highly resistant to noise. In addition, by analyzing the practices of several colleges and universities’ music schools, it is observed that there are significant differences among different institutions in the four comparative items, with standard deviations exceeding 0.6. In particular, the average scores using this study’s teaching system for vocal talent cultivation are 4.312, 4.335, 4.267, and 4.354, respectively, generally higher than those of the other colleges. The mean values of these four different vocal music majors were all above 4 points. Therefore, the teaching system for vocal talent training proposed in this paper effectively improves the quality of vocal talent training and provides a new path for the practical reform and optimization of said education.

Synergistic Coupling Mechanism of Digital Media Art and Vocal Talent Cultivation

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Synergistic Coupling Mechanism of Digital Media Art and Vocal Talent Cultivation"}

In the field of higher education in today’s era, cultivating vocal talents is crucial. This paper focuses on an innovative mechanism in vocal talent...

Synergistic Coupling Mechanism of Digital Media Art and Vocal Talent Cultivation

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 15 janv. 2024

Accepté: 22 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0582

Mots clés
Fourier algorithm, Music recognition system, Reinforcement learning model, Teaching aid system, Vocal talent cultivation

© 2024 Henghua Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Synergistic Coupling Mechanism of Digital Media Art and Vocal Talent Cultivation

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 15 janv. 2024

Accepté: 22 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0582

Mots clésFourier algorithm, Music recognition system, Reinforcement learning model, Teaching aid system, Vocal talent cultivation

© 2024 Henghua Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Fourier algorithm, Music recognition system, Reinforcement learning model, Teaching aid system, Vocal talent cultivation