Pervasive Diary in Music Rhythm Education: A Context-Aware Learning Tool Using Genetic Algorithm

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.

[1]  C. Bonwell,et al.  Active learning : creating excitement in the classroom , 1991 .

[2]  Edward J. Coyle,et al.  Perceptual Issues in Music Pattern Recognition: Complexity of Rhythm and Key Finding , 2001, Comput. Humanit..

[3]  Andrew Gartland-Jones,et al.  The Suitability of Genetic Algorithms for Musical Composition , 2003 .

[4]  Sandeep K. S. Gupta,et al.  Smart classroom: Enhancing collaborative learning using pervasive computing technology , 2003 .

[5]  Martin Dostál,et al.  Genetic Algorithms as a Model of Musical Creativity - on Generating of a Human-Like Rhythmic Accompaniment , 2005, Comput. Artif. Intell..

[6]  Alfonso Ortega,et al.  A fitness function for computer-generated music using genetic algorithms , 2006 .

[7]  Dragan Matic.  A GENETIC ALGORITHM FOR COMPOSING MUSIC , 2010 .

[8]  D. De,et al.  Polymorphism in Indian Classical Music: A pattern recognition approach , 2012, 2012 International Conference on Communications, Devices and Intelligent Systems (CODIS).

[9]  Frank Dürr,et al.  Context-aware and quality-aware algorithms for efficient mobile object management , 2012, Pervasive Mob. Comput..

[10]  Debashis De,et al.  Object oriented classification and pattern recognition of Indian Classical Ragas , 2012, 2012 1st International Conference on Recent Advances in Information Technology (RAIT).

[11]  D. De,et al.  An approach to identify Thhat of Indian Classical Music , 2012, 2012 International Conference on Communications, Devices and Intelligent Systems (CODIS).

[12]  Debashis De,et al.  Pattern Classification of Indian Classical Ragas based on Object Oriented Concepts , 2012 .

[13]  D. De,et al.  Inheritance in Indian Classical Music: An object-oriented analysis and pattern recognition approach , 2012, 2012 International Conference on Radar, Communication and Computing (ICRCC).

[14]  Debashis De,et al.  Quality measure model of music rhythm using Genetic Algorithm , 2012, 2012 International Conference on Radar, Communication and Computing (ICRCC).

[15]  Debashis De,et al.  Modeling high performance music computing using Petri Nets , 2014, Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC).

[16]  Debashis De,et al.  A Framework of Musical Pattern Recognition Using Petri Nets , 2014 .