Abstract
The existing research on data analysis and personalized recommendation algorithms for music mainly focuses on improving traditional recommendation systems such as classical collaborative filtering recommendation, content recommendation, and model recommendation. However, there has been no efficient solution to the cold start of recommendation systems and the sparse nature of scoring matrices. There is a dire need for an intelligent and efficient algorithm that can support the students in teaching music in an effective way. Deep belief neural network (DBN-DNN) is a deep learning model nested by many restricted Boltzmann machines (RBMs) to deal with sparse matrices and immediacy. At the same time, it can generate better initialization parameters and significantly improve the training speed of the model. This paper utilizes a decision support system that decides the matrix decomposition of user ratings by using a hidden semantic model to extract user preference features for hidden factors and the weights of music pieces on these k hidden factors. The proposed approach extracts the audio feature values from music clips by considering that music itself has classifiable information. This method is intelligent in terms of efficiency for effective music teaching and helps students learn. The experiments show that the personalized recommendation algorithm combined with a deep belief neural network has good recommendation performance. The recommendation coefficient predicted by the model is lower than the real rating for the samples with ratings. The recommendation is no longer limited to the original user group and song library and has better recommendation accuracy and scalability.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Armstrong R, Freitag D, Joachims T, et al. (1995) WebWatcher: A learning apprentice for the world wide Web, [J], In working notes of the AAAI spring symposium: Information gathering form heterogeneous, distributed environments, pp. 6–12
Besacier L, Ariyaeeinia AM, Mason JS et al (2004) Voice biometrics over the internet in the framework of COST action 275. EURASIP J Adv Signal Process 2004(4):466–479
Bussey M (2008) Embodied education: reflections on sustainable education. Sustain Collect 3:139–148
Contemplating a Constructivist Stance for Active Learning within Music Education. Arts Education Policy Review, 2011, 112(4):191–198.
Dolmans D. How theory and design-based research can mature PBL practice and research. Advances in Health Sciences Education, 2019, 24(1).
Dunlap JC, Sobel D, Sands DI (2007) Designing for deep and meaningful student-to-content interactions. Tech Trends 51(4):20–31
East ML, Havard BC (2015) Mental health mobile apps: from infusion to diffusion in the mental health social system. Jmir Mental Health 2(1):e10
Gamze S (2014) The effects of the development of metacognition on project-based learning - ScienceDirect. Procedia Soc Behav Sci 152(112):131–136
Lui KW, Leung F (2013) Curriculum traditions in Berlin and Hong Kong: a comparative case study of the implemented mathematics curriculum. ZDM Mathemat Educat 45(1):35–46
Nam J, Choi K, Lee J, Chou SY, Yang YH (2018) Deep learning for audio-based music classification and tagging: Teaching computers to distinguish rock from bach. IEEE Signal Process Mag 36(1):41–51
Ngafook N (2005) A curriculum of mother-son plots on education’s center stage. Caddogappress 21(4):43
Pan L, Chen P (2020) Research on language-teaching materials—an evaluation of extensive reading textbooks. Theory Practice Language Stud 10(12):1628
Petchauer E (2011) I feel what he was doin’ responding to justice-oriented teaching through hip-hop aesthetics. Urban Educat 46(6):1411–1432
Rodríguez C, Gutiérrez J, Pozo T (2010) An empirical study of presage variables in the teaching-learning of statistics, in the light of research on competencies. Electron J Res Educational Psychol 8(1):235–262
Try to See It My Way (2012) The Discursive Function of Idiosyncratic Mathematical Metaphor. Math Think Learn, 14(1):55-80
Wang X, Chen Y (2021) Music teaching platform based on FPGA and neural network. Microprocess Microsys 80:103337
Whitenack DA, Swanson PE (2013) The transformative potential of boundary spanners: A narrative inquiry into preservice teacher education and professional development in an NCLB-impacted context. Education Policy Analysis Archives 21:19
Zhang XL, Wu J (2012) Deep belief networks based voice activity detection. IEEE Trans Audio Speech Lang Process 21(4):697–710
Zhong K, Wang Y, Pei J et al (2021) Super efficiency SBM-DEA and neural network for performance evaluation. Infor Process Manage 58(6):102728
Funding
The paper did not receive any financial support.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
The paper does not deal with any ethical problems.
Informed consent
We declare that all authors have informed consent.
Additional information
Communicated by Tiancheng Yang.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Tang, C., Zhang, J. An intelligent deep learning-enabled recommendation algorithm for teaching music students. Soft Comput 26, 10591–10598 (2022). https://doi.org/10.1007/s00500-021-06709-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-021-06709-x