Abstract
Score reduction is a process that arranges music for a target instrument by reducing original music. In this study we present a music arrangement framework that uses score reduction to automatically arrange music for a target instrument. The original music is first analyzed to determine the type of arrangement element of each section, then the phrases are identified and each is assigned a utility according to its type of arrangement element. For a set of utility-assigned phrases, we transform the music arrangement into an optimization problem and propose a phrase selection algorithm. The music is arranged by selecting appropriate phrases satisfying the playability constraints of a target instrument. Using the proposed framework, we implement a music arrangement system for the piano. An approach similar to Turing test is used to evaluate the quality of the music arranged by our system. The experiment results show that our system is able to create viable music for the piano.
- Berndt, A., Hartmann, K., Rober, N., and Masuch, M. 2006. Composition and arrangement techniques for music in interactive immersive environments. In Proceedings of the Audio Mostly Conference.Google Scholar
- Boser, B., Guyon, I., and Vapnik, V. 1992. A training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual Workshop on Computational Learning Theory. Google Scholar
Digital Library
- Brucker, P. and Nordmann, L. 1994. The k-track assignment problem. SIAM J. Comput. 52.Google Scholar
- Cambouropoulos, E. 2001. The local boundary detection model (LBDM) and its application in the study of expressive timing. In Proceedings of the International Computer Music Conference (ICMC'01).Google Scholar
- Chung, J. W. 2006. The affective remixer: Personalized music arranging. In Proceedings of the Conference on Human Factors in Computing Systems (ACM SIGCHI'06). Google Scholar
Digital Library
- Corozine, V. 2002. Arranging Music for the Real World. Mel Bay.Google Scholar
- Daniel, R. and Potter, W. D. 2006. GA-based music arranging for guitar. In Proceedings of the International Congress on Evolutionary Computation (CEC'06).Google Scholar
- Hastie, T. and Tibshrani, R. 1998. Classification by pairwise coupling. Ann. Stat. 26, 2.Google Scholar
- Jones, N. C. and Pevzner, P. A. 2004. An Introduction to Bioinformatics Algorithms. MIT Press.Google Scholar
- Kasimi, A. A., Nechols, E., and Raphael, C. 2007. A simple algorithm for automatic generation of polyphonic piano fingerings. In Proceedings of the International Conference on Music Information Retrieval (ISMIR'07).Google Scholar
- Kasimi, A. A., Nechols, E., and Raphael, C. 2005. Automatic fingering system (AFS). In Proceedings of the International Conference on Music Information Retrieval (ISMIR'05).Google Scholar
- Lui, S., Horner, A., and Ayers, L. 2006. MIDI to SP-MIDI transcoding using phrase stealing. IEEE Multimedia 13, 2. Google Scholar
Digital Library
- Miranda, E. R. 2001. Composing Music with Computers. Focal Press. Google Scholar
Digital Library
- Nagashima, T. and Kawashima, J. 1997. Experimental study on arranging music by chaotic neural network. Int. J. Intell. Syst. 12, 4.Google Scholar
Cross Ref
- Owsinski, B. 1999. The Mixing Engineer's Handbook. Thomson Course Technology.Google Scholar
- Pearce, M. and Wiggins, G. 2001. Towards a framework for the evaluation of machine Compositions. In Proceedings of the Symposium on Artificial Intelligence and Creativity in the Arts and Sciences (AISB'01).Google Scholar
- Rimsky-Korsakov, N. A. 1888. Sheherazade, Op. 35 (Piano Reduction). G. Schirmer, Inc.Google Scholar
- Sorensen, A. and Brown, A. R. 2000. Introducing jMusic. In Proceedings of the Australasian Computer Music Conference.Google Scholar
- Stein, L. 1979. Structure & Style: The Study and Analysis of Musical Forms. Summy-Birchard Music.Google Scholar
- Tuohy, D. R. and Potter, W. D. 2006. An evolved neural network/HC hybrid for tablature creation in GA-based guitar arranging. In Proceedings of the International Computer Music Conference (ICMC'06).Google Scholar
- White, G. 1992. Instrumental Arranging. McGraw-Hill.Google Scholar
- Witten, I. H. and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. Google Scholar
Digital Library
- Yonebayashi, Y., Kameoka, H., and Sagayama, S. 2007. Automatic decision of piano fingering based on hidden Markov models. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'07). Google Scholar
Digital Library
Index Terms
Towards an automatic music arrangement framework using score reduction
Recommendations
Automatic System for the Arrangement of Piano Reductions
ISM '09: Proceedings of the 2009 11th IEEE International Symposium on MultimediaPiano reduction is a process that arranges music for the piano by reducing the original music into the most basic components. In this study we present an automatic arrangement system for piano reduction that arranges music algorithmically for the piano ...
Automatic Guitar Music Transcription
ACSAT '12: Proceedings of the 2012 International Conference on Advanced Computer Science Applications and TechnologiesThis paper presents a system that helps in automatically generating guitar tablatures and musical scores based on musical audio data. Information gathered from the audio consists of pitch, onsets and durations, chords, and beat and tempo. Major issues ...
Automatic music composition using answer set programming
Music composition used to be a pen and paper activity. These days music is often composed with the aid of computer software, even to the point where the computer composes parts of the score autonomously. The composition of most styles of music is ...






Comments