Abstract
The traditional vocal music teaching model has been unable to meet the needs of today’s vocal music teaching, and the promotion and improvement of the online vocal music teaching model is imperative. Due to the influence of various factors (online equipment, environmental noise, etc.), there are certain defects in the timbre of online vocal music teaching, which cannot guarantee the effect of vocal music teaching. Preprocess the timbre signal of online vocal music teaching (remove mute segment, pre-emphasis and windowing), and based on this, extract timbre signal features (time domain feature, frequency domain feature and cepstral domain feature). In this paper, a feature comparison model of timbre signal is constructed to reduce the dimension of timbre signal feature vector. It adopts SAGA algorithm to determine the timbre evaluation formula of online vocal music teaching, and realizes the evaluation of timbre of online vocal music teaching. The experimental data show that compared with the reference standard, the complete rate of timbre signal feature extraction and the correct rate of timbre evaluation obtained by the proposed method are higher. The experimental results fully confirm the effectiveness and feasibility of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Iliaki, G., Velentzas, A., Michailidi, E., et al.: Exploring the music: a teaching-learning sequence about sound in authentic settings. Res. Sci. Technol. Educ. 37(2), 218–238 (2019)
Song, R.: Research on the application of computer multimedia music system in college music teaching. J. Phys. Conf. Ser. IOP Pub. 1744(3), 032214 (2021)
Ge, Z.: Design and implementation of college music teaching management system. In: 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), pp. 513–516. IEEE (2021)
Hou, J.-W., Jia, K., Jiao, X.-J.: Teaching evaluation on a WebGIS course based on dynamic selfdaptive teachingearningased optimization. J. Central South Univ. 26(3), 640–653 (2019)
Meskill, C., Anthony, N., Sadykova, G.: Teaching languages online: professional vision in the making. Lang. Learn. Technol. 24(3), 160–175 (2020)
Li, R., Zhang, M.: Singing-voice timbre evaluations based on transfer learning. Appl. Sci. 12(19), 9931 (2022)
Fautley, M., Kinsella, V., Whittaker, A.: Models of teaching and learning identified in whole class ensemble tuition. Br. J. Music Educ. 36(3), 243–252 (2019)
Tang, B.: Diversified development of national vocal music teaching based on online teaching system. In: 2021 4th International Conference on Information Systems and Computer Aided Education, pp. 269–273 (2021)
Sun, M.Q.: Simulation of digital audio music recognition based on time-frequency domain information extraction. Comp. Simul. 38(07), 415–418+428 (2021)
Liu, C., Wan, P., Tu, Y.F., et al.: A WSQ-based mobile peer assessment approach to enhancing university students’ vocal music skills and learning perceptions. Australas. J. Educ. Technol. 37(6), 1–17 (2021)
Funding
1. Excellent Top-notch Talent Cultivation Project of Universities in Anhui Province: Research on the Construction of inner Vision in Vocal Music Singing under the Vision of “The Belt and Road” (GXYQ2020164)
2. Humanities and Social Science Key Projects of Education Department of Anhui Province:Research on the Transmutation of Female Characters in Contemporary Chinese National Opera (SK2020A327)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, R., Qi, J., Qiao, D. (2022). An Online Vocal Music Teaching Timbre Evaluation Method Based on Feature Comparison. In: Fu, W., Sun, G. (eds) e-Learning, e-Education, and Online Training. eLEOT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-21164-5_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-21164-5_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21163-8
Online ISBN: 978-3-031-21164-5
eBook Packages: Computer ScienceComputer Science (R0)