Abstract
Rhythm is the combination of beats which is most essential ingredient of music that contains the length of each note in a music composition. Knowledge of Rhythm structures and their application for new Music generation is very difficult task for the students of Musicology and the ratio between music teachers and music students is very low. In this paper a mechanism is introduced that efficiently selects the parent rhythms for creating offspring rhythm using Genetic Algorithm Optimization in Pervasive Education. Advancement of sensor technology and the wide use of social network services, music learning is now very easy comparing to earlier days. In this contribution m-learning is also selected to refer specifically to learning facilitated by mobile devices such as Personal Digital assistant (PDA) and mobile phones. The primary aim of m-learning is to provide the users with a learning environment which is not restricted to a specific location or time. Compared to a traditional classroom setting, m-learning increases the mobility of a learner, allowing him/her to learn. The ultimate goal of this study is to create a context awareness intelligent system tool for music rhythm creation for the application in Pervasive Teaching-Learning process. A software tool has also been implemented PERVASIVE DIARY to establish the processing algorithm.
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
Preview
Unable to display preview. Download preview PDF.
References
Kurt, R., Stephan, S., Ralph, L., Frank, D., Tobiasm, F.: Context-aware and quality-aware algorithms for efficient mobile object management. Elsevier Journal of Pervasive and Mobile Computing, 131–146 (2012)
Bonwell, C., Eison, J.: Active learning: Creating excitement in the classroom. ASHE-ERIC Higher Education Report No. 1, George Washington University, Washington, DC (1991)
Yau, S.S., Gupta, S.K.S., Karim, F., Ahamed, S.I., Wang, Y., Wang, B.: Smart Classroom: Enhancing Collaborative Learning Using Pervasive Computing Technology. In: Proceedings of Annual Conference of American Society of Engineering Education (2003)
Shmulevich, I., Yli-Harja, O., Coyle, E.J., Povel, D.: Lemstrm K.: Perceptual Issues in Music Pattern Recognition Complexity of Rhythm and Key Finding. In: Computers and the Humanities, Kluwer Academic Publishers, pp. 23–35 (2001)
Chakraborty, S., De, D.: Pattern Classification of Indian Classical Ragas based on Object Oriented Concepts. International Journal of Advanced Computer engineering & Architecture 2, 285–294 (2012)
Gartland-Jones, A., Copley, P.: The Suitability of Genetic Algorithms for Musical Composition. Contemporary Music Review 22(3), 43–55 (2003)
Matic, D.: A Genetic Algorithm for composing Music. Proceedings of the Yugoslav Journal of Operations Research 20(1), 157–177 (2010)
Dostal, M.: Genetic Algorithms as a model of musical creativity – on generating of a human-like rhythimic accompainment. Computing and Informatics 22, 321–340 (2005)
Chakraborty, S., De, D.: Object Oriented Classification and Pattern Recognition of Indian Classical Ragas. In: Proceedings of the 1st International Conference on Recent Advances in Information Technology (RAIT), pp. 505–510. IEEE (2012)
Alfonceca, M., Cebrian, M., Ortega, A.: A Fitness Function for Computer-Generated Music using Genetic Algorithms. WSEAS Trans. on Information Science & Applications 3(3), 518–525 (2006)
De, D., Roy, S.: Polymorphism in Indian Classical Music: A pattern recognition approach. In: Proceedings of International Conference on communications, Devices and Intelligence System (CODIS), pp. 632–635. IEEE (2012)
De, D., Roy, S.: Inheritance in Indian Classical Music: An object-oriented analysis and pattern recognition approach. In: Proceedings of International Conference on RADAR, Communications and Computing (ICRCC), pp. 296–301. IEEE (2012)
Bhattacharyya, M., De, D.: An approach to identify thhat of Indian Classical Music. In: Proceedings of International Conference of Communications, Devices and Intelligence System (CODIS), pp. 592–595. IEEE (2012)
Chakrabarty, S., De, D.: Quality Measure Model of Music Rhythm Using genetic Algorithm. In: Proceedings of the International Conference on RADAR, Communications and Computing (ICRCC), pp. 203–208. IEEE (2012)
Roy, S., Chakrabarty, S., Bhakta, P., De, D.: Modelling High Performing Music Computing using Petri Nets. In: Proceedings of International Conference on Control, Instrumentation, Energy and Communication, pp. 757–761. IEEE (2013)
Roy, S., Chakrabarty, S., De, D.: A Framework of Musical Pattern Recognition using Petri Nets. Accepted In: Emerging Trends in Computing and Communication 2014, Springer-Link Digital Library, Submission No- 60 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Chakrabarty, S., Roy, S., De, D. (2014). Pervasive Diary in Music Rhythm Education: A Context-Aware Learning Tool Using Genetic Algorithm. In: Kumar Kundu, M., Mohapatra, D., Konar, A., Chakraborty, A. (eds) Advanced Computing, Networking and Informatics- Volume 1. Smart Innovation, Systems and Technologies, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-07353-8_77
Download citation
DOI: https://doi.org/10.1007/978-3-319-07353-8_77
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07352-1
Online ISBN: 978-3-319-07353-8
eBook Packages: EngineeringEngineering (R0)