Advertisement

An Intelligent Composition Algorithm for Automatic Thematic Music Generation from Extant Pieces

  • Abhijit Suprem
  • Manjit Ruprem
Conference paper

Abstract

Recently, research on computer based music generation utilizing composition algorithm has drawn attention. The goal of this research is to produce new music, never heard before, using the algorithm developed and presented in this paper. The developed algorithm uses learning technique and probability and statistical analysis. The algorithm uses note sequences and other musical parameters such as note length, pitch, accidentals, modifications (intensity, speed), and note sequence repetition density for the preparation of a probability table that will generate new music. We used thematic music pieces (from same theme) as input music for analysis using learning followed by statistical analysis. We used MATLAB for analysis and MC Music editor for display. This research study is the first of its kind to create thematic music pieces effectively in a computer-based environment. The outcome of this research has a wide range of usage: waiting-music during automated phone-calls, background music in airports, airplanes, and restaurants, and so on. The work was extended to include variations of frequency and the shape of the note sequences for analysis.

Keywords

Machine learning MATLAB Music generation Music theory Pattern recognition Statistical analysis 

Notes

Acknowledgment

I would like to acknowledge Dr. Honora Chapman, Director, Smittcamp Family Honors College, for her advice and encouragement. I would also like to acknowledge Dr. Nagy Bengiamin, Chair of Electrical and Computer Engineering and Dr. Ram Nunna, Dean of Lyles College of Engineering for providing the computing facilities for this research. I also acknowledge my parents for allowing us to accomplish part of this research in a home computing system.

References

  1. 1.
    D. Plans, D. Morelli, Experience-driven procedural music generation for games. IEEE Trans. Comput. Intell. AI Games 4(3), 192–198 (2012)CrossRefGoogle Scholar
  2. 2.
    W. Schulze, B. van der Merwe, Music generation with Markov models. IEEE Multimedia 18(3), 78–85 (2011)CrossRefGoogle Scholar
  3. 3.
    J. Leach, J. Fitch, Nature, music, and algorithmic composition. Comput. Music J. 19(2), 23–33 (1995)CrossRefGoogle Scholar
  4. 4.
    J.A. Whittaker, M. Thomason, A Markov chain model for statistical software testing. IEEE Trans. Softw. Eng. 20(10), 812–824 (1994)CrossRefGoogle Scholar
  5. 5.
    Q. Yuting, J. Paisley, L. Carin, Music analysis using hidden Markov mixture models. IEEE Trans. Signal Process 55(11), 5209–5224Google Scholar
  6. 6.
    J. Mishra, Classification of linear fractals through L-system. First Int. Conf. Emerg. Trends Eng. Tech. 1(5), 16–18 (2008)Google Scholar
  7. 7.
    P. Meyer, The fractal dimension of music. Senior Thesis, Columbia University (1993)Google Scholar
  8. 8.
    A. Jain, R. Duin, M. Jianchang, Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)CrossRefGoogle Scholar
  9. 9.
    G. Recktenwald, Numerical Methods with MATLAB: Implementations and Applications (Prentice Hall, Upper Saddle River, 2000)Google Scholar
  10. 10.
    V. Shen, C. Yue-Shan, T. Juang, Supervised and unsupervised learning by using Petri nets. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 40(2), 363–375 (2010)CrossRefGoogle Scholar
  11. 11.
    K. Dixon, C. Lippitt, J. Forsythe, Supervised machine learning for modeling human recognition of vehicle-driving situations. Int. Conf. Intell. Robots Syst. 2(6), 604–609 (2005)Google Scholar
  12. 12.
    P. Kulkarni, Introduction to Reinforcement and Systemic Machine Learning. Reinforcement and Systemic Machine Learning for Decision Making. (IEEE, Piscataway, 2012), pp. 1–21Google Scholar
  13. 13.
    G. Maozu, L. Yang, J. Malec, A new Q-learning algorithm based on the metropolis criterion. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 34(5), 2140–2143 (2004)CrossRefGoogle Scholar
  14. 14.
    XML Documents, MATLAB documentation center—data import and export, http://www.mathworks.com/help/matlab/ref/xmlread.html
  15. 15.
    A. Suprem, M. Ruprem, A new composition algorithm for automatic generation of thematic music from existing music pieces., Lecture Notes in Engineering and Computer Science, in Proceedings of The World Congress on Engineering and Computer Science 2013, WCECS 2013, USA, San Francisco, 23–25 Oct 2013, pp. 808–812 Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringLyles College of Engineering, California State UniversityFresnoUSA
  2. 2.Buchanan High SchoolFresnoUSA

Personalised recommendations