| Formal models for analyzing and creating music (CS-BWP-AI, CS-MWP-AI) |
| DozentIn:Prof. Dr. phil. Kai-Uwe Kühnberger |
| Veranstaltungstyp:Seminar (Offizielle Lehrveranstaltungen) |
| Ort:93/E07 |
| Semester:SoSe 2024 |
Zeiten:Fr. 14:00 - 16:00 (wöchentlich) - Seminar Sitzung
Erster Termin:Freitag, 05.04.2024 14:00 - 16:00, Ort: 93/E07 |
| Beschreibung:This course will give an overview of formal frameworks for analyzing and creating music. Examples of such frameworks are grammar theory, pattern recognition, (deep) learning etc. There are no special requirements, although basic knowledge in mathematics, computer science, and music theory would be helpful. |
LiteraturSome examples for potential papers can be found here. The precise list of papers will be specified in the first meeting.
- Andreatta & Baroin (2016): An Introduction on Formal and Computational Models in Popular Music
- Cohn, R. (1998). Introduction to Neo-Riemannian Theory: A Survey and a Historical Perspective. Journal of Music Theory 42:167-180.
- Dai, Jin, Gomes, Dannenberg (2021): Controllable deep melody generation via hierarchical music structure representation, Arxiv
- Lehman (2014): Film Music and Neo-Riemannian Theory
- Miranda et al (2021): A Quantum Natural Language Processing Approach to Musical Intelligence
- Rohrmeier (2022): On Creativity, Music’s AI Completeness, and Four Challenges for Artificial Musical Creativity
- Robert et al. (2018): Variational Autoencoders for long-term Structure in Music
- Pachet, F., Roy, P., & Carré, B. (2020). Assisted music creation with Flow Machines: Towards new categories of new. ArXiv:2006.09232 [Cs, Eess]. http://arxiv.org/abs/ 2006.09232
- Wu, J., Liu, X., Hu, X. & Zhu, J. (2020): PopMNet: Generating structured pop music melodies using neural networks, Art. Intelligence 286 |
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