Abstract
This article aims to lay a foundation for learning and practicing music online. Massive Open Online Courses (MOOCs) are growing as we are moving to online classes. Current music courses through MOOCs mostly focus on peer evaluation for assessing the students’ performance. However, this technique may not be practical when it is applied to larger class sizes. Therefore, in this research, the main goal is to reduce the instructor’s load and provide online real-time performance feedback. As a contribution to music education, we propose a new technological framework to automate music lessons for learning how to play any favorite songs via existing machine learning (ML) techniques for adaptability to various learning styles.
We discuss the main problems with existing online music lessons that ML techniques can resolve:
-
1.
Finding or developing a music lesson based on the student’s learning style, musical background, or preference.
-
2.
Providing quantitative and qualitative assessments of the student’s performance.
The paper discusses the tools for facilitating assessment where there is a semi-automatic assessment system that can train itself based on the instructors’ real-life assessments on a small group and further assess larger sets of performances.
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
Notes
- 1.
Social constructivism: Social constructivism teaches that all knowledge develops as a result of social interaction and language use, and is therefore a shared, rather than an individual, experience. Knowledge is additionally not a result of observing the world, it results from many social processes and interactions.
References
Bozkurt, B., Gulati, S., Romani Picas, O., Serra, X.: MusicCritic: a technological framework to support online music teaching for large audiences. In: Forrest, D. (ed.) Proceedings of the International Society for Music Education. 33rd World Conference on Music Education (ISME), Baku, Azerbaijan, 15–20 July 2018. International Society for Music Education, Malvern (2018)
Jurafsky, D., Martin, J.H.: Sequence labeling for parts of speech and named entities. In: Speech and Language Processing (2020)
Jamshidi, F., Marghitu, D.: A web-based platform to teach music online (2020)
Jamshidi, F., Marghitu, D.: Using music to foster engagement in introductory computing courses. In: Proceedings of the 50th ACM Technical Symposium on Computer Science Education, SIGCSE 2019, p. 1278. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3287324.3293855
Johnson, C.: Developing a teaching framework for online music courses (2016)
Lin, Y.J., Kao, H.K., Tseng, Y.C., Tsai, M., Su, L.: A human-computer duet system for music performance. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 772–780 (2020)
MĂĽller, M.: Fundamentals of Music Processing: Audio, Analysis, Algorithms, Applications. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21945-5
Muller, M., Ellis, D.P., Klapuri, A., Richard, G.: Signal processing for music analysis. IEEE J. Sel. Top. Sig. Process. 5(6), 1088–1110 (2011)
Orio, N., Lemouton, S., Schwarz, D.: Score following: state of the art and new developments. In: New Interfaces for Musical Expression (NIME) (2003)
Sephus, N.H., Lanterman, A.D., Anderson, D.V.: Exploring frequency modulation features and resolution in the modulation spectrum. In: 2013 IEEE Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), pp. 169–174. IEEE (2013)
Sephus, N.H., Olubanjo, T.O., Anderson, D.V.: Enhancing online music lessons with applications in automating self-learning tutorials and performance assessment. In: 2013 12th International Conference on Machine Learning and Applications, vol. 2, pp. 568–571. IEEE (2013)
Soulez, F., Rodet, X., Schwarz, D.: Improving polyphonic and poly-instrumental music to score alignment (2003)
Uhle, C., Dittmar, C., Sporer, T.: Extraction of drum tracks from polyphonic music using independent subspace analysis. In: Proceedings of the ICA, pp. 843–847 (2003)
Xia, G.: Expressive collaborative music performance via machine learning (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Jamshidi, F., Marghitu, D., Chapman, R. (2021). Developing an Online Music Teaching and Practicing Platform via Machine Learning: A Review Paper. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. Access to Media, Learning and Assistive Environments. HCII 2021. Lecture Notes in Computer Science(), vol 12769. Springer, Cham. https://doi.org/10.1007/978-3-030-78095-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-78095-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78094-4
Online ISBN: 978-3-030-78095-1
eBook Packages: Computer ScienceComputer Science (R0)