Accès libre

Strategies for the transmission of ethnic music culture in college music education based on the background of big data

   | 07 août 2023
À propos de cet article

Citez

Xu, Y. (2022). The new media environment presents challenges and opportunities for music education in higher education. Journal of Environmental and Public Health, 2022.Search in Google Scholar

Bond, V. L. (2017). Culturally responsive education in music education: A literature review. Contributions to Music Education, 42, 153-180.Search in Google Scholar

Jiang, N., Jin, S., Duan, Z., et al. (2020). When counterpoint meets Chinese folk melodies. Advances in Neural Information Processing Systems, 33, 16258-16270.Search in Google Scholar

Ber, S. (2019). Chinese folk music in the context of the modern strategy of “cultural industry.” Danish Scientific Journal, (30), 3-7.Search in Google Scholar

Han, Y. T., Yin, A. Q., Education, F. O., et al. (2018). Reflections on the Cultural Inheritance of Folk Music in Music Education. Journal of Northeast Normal University (Philosophy and Social Sciences).Search in Google Scholar

Zhang, S. X., Province, G., City, F., et al. (2019). Exploring the New Path of Folk Music Education— Taking Shunde Folk Music Education as an Example. Education Teaching Forum.Search in Google Scholar

Han, R., & Leung, B. W. (2017). A survey on Weifang teachers’ attitudes toward teaching Chinese folk music. International Journal of Music Education, 35(2), 202-215.Search in Google Scholar

Zheng, X., Wang, L., Li, D., et al. (2017). Algorithm composition of Chinese folk music based on swarm intelligence. International Journal of Computing Science and Mathematics, 8(5), 437-446.Search in Google Scholar

Wu, Q. (2021). Pressure source of athletes in the field based on an improved hierarchical k-means algorithm. International Journal of Electrical Engineering Education, 002072092098504.Search in Google Scholar

Yunsick, Sung, Haitao, et al. (2018). Motion quaternion-based motion estimation method of MYO using the K-means algorithm and Bayesian probability. Soft Computing: A Fusion of Foundations, Methodologies, and Applications.Search in Google Scholar

Borlea, I. D., Precup, R. E., Borlea, A. B., et al. (2021). A Unified Form of Fuzzy C-Means and K-Means algorithms and its Partitional Implementation. Knowledge-Based Systems, Feb. 28.Search in Google Scholar

Fonseca, P., Massimo, T., Angela, C. (2021). 27 Machine Learning Algorithms Based on Haplotype Libraries for Classification of Stillbirth Susceptibility in Holstein Cows. Journal of Animal Science, Supplement_3.Search in Google Scholar

Sun, Jie, Fujita, et al. (2017). Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble. Knowledge-Based Systems.Search in Google Scholar

Liu, B., Liu, C., Xiao, Y., et al. (2022). AdaBoost-based transfer learning method for positive and unlabelled learning problem. Knowledge-Based Systems, Apr. 6.Search in Google Scholar

Sun, B., Chen, S., Wang, J., et al. (2016). A robust multi-class AdaBoost algorithm for mislabeled noisy data. Knowledge-Based Systems.Search in Google Scholar

Wang, Y., Sherry Ni, X. (2019). A XGBoost risk model via feature selection and Bayesian hyper-parameter optimization. International Journal of Database Management Systems, 11(01), 01-17.Search in Google Scholar

Linh, N. T. T., Pandey, M., Janizadeh, S., Bhunia, G. S., Norouzi, A., Ali, S., et al. (2022). Flood susceptibility modeling based on a new hybrid intelligence model: optimization of XGBoost model using GA metaheuristic algorithm. Advances in Space Research, 69(9), 3301-3318.Search in Google Scholar

eISSN:
2444-8656
Langue:
Anglais
Périodicité:
Volume Open
Sujets de la revue:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics