Human and computation-based musical representation for gamelan music

Arry Maulana Syarif

Abstract

A public database containing representative data of karawitan traditional music is needed as a resource for researchers who study computer music and karawitan. To establish this database, a text-based pitch model for music representation that is both human and computer-based was first investigated. A new model of musical representation that can be read by humans and computers is proposed to support music and computer research on karawitan also known as gamelan music. The model is expected to serve as the initial effort to establish a public database of karawitan music representation data. The proposed model was inspired by Helmholtz Notation and Scientific Pitch Notation and well-established, text-based pitch representation systems. The model was developed not only for pitch number, high or low or middle pitch information (octave information), but for musical elements found in gamelan sheet music pieces that include pitch value and legato signs. The model was named Gendhing Scientific Pitch Notation (GSPN). Ghending is a Javanese word that means “song”. The GSPN model was designed to represent music by formulating musical elements from a sheet music piece. Furthermore, the model can automatically be converted to other music representation formats. In the experiment, data in the GSPN format was implemented to automatically convert sheet music to a binary code with localist representation technique.

References

Becker, J. (1980). Traditional Music in Modern Java: Gamelan in a Changing Society. The University Press of Hawaii. https://doi.org/10.2307/j.ctv9zcjt8.9

Becker, J., & Becker, A. (1982). A Grammar of the Musical Genre Srepegan. University of Texas Press.

Colombo, F., Muscinelli, S.P., Seeholzer, A., Brea, J., & Gerstner, W. (2016). Algorithmic Composition of Melodies with Deep Recurrent Neural Networks. Proceedings of 1st Conference on Computer Simulation of Musical Creativity, UK, 1, 2-12.

Downie, J. S. (2003). Music Information Retrieval. Annual Review of Information Science and Technology, 37(1), 295-340.

Eigenfeldt, A., & Pasquier, P. (2010). Realtime Generation of Harmonic Progressions Using Controlled Markov Selection. Proceedings of the First International Conference on Computational Creativity, Portugal, 1,16-25.

Good, M. (2013). Exchange and Publish Scores with MusicXML. https://www.musicxml.com/publications/

Hastuti, K., Azhari, A., Musdholifah, A., & Supanggah, R. (2016). Building Melodic Feature Knowledge of Gamelan Music Using Apriori Based on Functions in Sequence (AFiS) Algorithm. International Review on Computers and Software, 11(12), 1127-1137. https://doi.org/10.15866/irecos.v11i12.10841

Hastuti, K., Azhari, A., Musdholifah, A., & Supanggah, R. (2017). Rule-Based and Genetic Algorithm for Automatic Gamelan Music Composition. International Review on Modelling and Simulations, 10(3), 2010-212. https://doi.org/10.15866/iremos.v10i3.11479

Hild, H., Feulner, J., & Menzel, W. (1991). HARMONET: A Neural Net for Harmonizing Chorales in the Style of J.S.Bach. Proceedings of Advances in Neural Information Processing Systems, 4, 267-274.

Hughes, D. W. (1988). Deep Structure and Surface Structure in Javanese Music: A Grammar of Gendhing Lampah. University of Illinoi Press.

Keith, S. (2010). Bridging the Gap: Thoughts on Computer Music and Contemporary (Popular) Electronic Music. Proceedings of the 2010 Australasian Computer Music Conference, Australia, 2010, 37-42.

Liang, F., Gotham, M., Johnson, M., & Shotton, J. (2017). Automatic Stylistic Composition of Bach Chorales with Deep LSTM. 18th International Society for Music Information Retrieval Conference, Suzhou, China, 18, 449-456.

Makris D., Kaliakatsos-Papakostas M., Karydis I., & Kermanidis K.L. (2017). Combining LSTM and Feed Forward Neural Networks for Conditional Rhythm Composition. In Boracchi G., Iliadis L., Jayne C., &

Likas A. (Eds.), Engineering Applications of Neural Networks. EANN 2017: vol. 744. Communications in Computer and Information Science (pp. 570-582). Springer. https://doi.org/10.1007/978-3-319- 65172-9_48

Pachet, F., & Roy, P. (2011). Markov constraints: Steerable generation of Markov sequences. Constraints, 16, 148-172.

Park, H., Yoo, C. (2017). Melody Extraction and Detection through LSTM-RNN with Harmonic Sum Loss. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017, 2766-2770, https://doi.org/10.1109/ICASSP.2017.7952660

Surjodiningrat, W., Khandelwal V.K., & Soesianto, F. (1977). Gamelan dan Komputer: Analisa Patet dan Komposisi Gending Jawa Laras Slendro. Universitas Gadjah Mada.

Todd, P. M. (1989). A Connectionist Approach to Algorithmic Composition. Computer Music Journal, 13(4), 27-43.

Trueman, D. (2007). Why a Laptop Orchestra. Cambridge Journal, 12, 171 – 179.

Yi, L., & Goldsmith, J. (2007). Automatic Generation of Four-part Harmony. Proceedings of the Fifth UAI Conference on Bayesian Modeling Applications Workshop, Canada, 268, 81–86.

Zhou, X., & Lerch, A. (2015). Chord Detection using Deep Learning. 16th International Society for Music Information Retrieval Conference, USA, 16, 52-58.