Autors: Pleshkova, S. G., Kostov K. Title: Generative Markov Chain Model Development for Guitar Melodies Creation Keywords: Generative model, Guitar melody, Markov chains, Music generationAbstract: This study proposes a generative Markov Chain model for creating guitar melodies. The proposed model generates various transition matrices for the Markov chain using multiple chord progressions in different genres. When analyzing the various transition states, separate transition matrices are created for each genre, each with an N-chord sequence used as a context. The purpose of the model is to generate stylistically consistent and accurate guitar chord progressions across various genres for creating guitar melodies. References - Gale, E., Matthews, O., Costello, B. de L., & Adamatzky, A. (2013). Beyond Markov Chains, Towards Adaptive Memristor Network-based Music Generation (No. arXiv:1302.0785). arXiv. https://doi.org/10.48550/arXiv.1302.0785
- Briot, J.-P., Hadjeres, G., & Pachet, F.-D. (2020). Deep Learning Techniques for Music Generation. Springer International Publishing. https://doi.org/10.1007/978-3-319-70163-9
- Shapiro, I., & Huber, M. (2021). Markov Chains for Computer Music Generation. Journal of Humanistic Mathematics, 11(2), 167–195. https://doi.org/10.5642/jhummath.202102.08
- Xu, Y. (2023). Music Generator Applying Markov Chain and Lagrange Interpolation. Highlights in Science, Engineering and Technology, 39, 266–273. https://doi.org/10.54097/hset.v39i.6538
- Chang, J. (2007). Stochastic processes. Yale University. http://www.stat.yale.edu/~pollard/Courses/251.spring2013/Handouts/Chang-notes.pdf
- Ferretti, S. (2018). On the Complex Network Structure of Musical Pieces: Analysis of Some Use Cases from Different Music Genres. Multimedia Tools and Applications, 77(13), 16003–16029. https://doi.org/10.1007/s11042-017-5175-y
- Müller, M. (2015). Fundamentals of Music Processing: Audio, Analysis, Algorithms, Applications. Springer International Publishing. https://doi.org/10.1007/978-3-319-21945-5
- Kantarelis, S., Thomas, K., Lyberatos, V., Dervakos, E., & Stamou, G. (2024). CHORDONOMICON: A Dataset of 666,000 Songs and their Chord Progressions (No. arXiv:2410.22046). arXiv. https://doi.org/10.48550/arXiv.2410.22046
Issue
| National Conference with International Participation, TELECOM, 2026, Bulgaria, https://doi.org/10.1109/TELECOM66943.2025.11304051 |
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