| Title |
Predicting Mozart's next note via echo state networks / |
| Authors |
Krušna, Ąžuolas ; Lukoševičius, Mantas |
| Full Text |
|
| Is Part of |
CEUR workshop proceedings: System 2018: Symposium for young scientists in technology, engineering and mathematics: proceedings of the symposium for young scientists in technology, engineering and mathematics, Gliwice, Poland, May 28, 2018 / edited by G. Capizzi, R. Damaševičius, A. Lopata, T. Krilavičius, Ch. Napoli, M. Woźniak.. Aachen : CEUR-WS. 2018, vol. 2147, p. 84-91 |
| Keywords [eng] |
algorithtmic composition ; echo state network ; MIDI ; recurrent neural network |
| Abstract [eng] |
Even though algorithmic music has been around the world since the old days, it has never attracted as many researchers as in the recent years. To our knowledge it existed in Iran back in the Middle Ages and in Europe during the Age of Enlightenment. Though the form has changed and it has grown layers of complexity, the very foundations of the algorithm that generates musical compositions have not changed, i.e. most of them are based on structures of fortuity. Additionally, models that are able to learn have been discovered allowing us to imitate the music of the incredible artists throughout history. The thought alone is crazy to think of and seems to be from the sci-fi. In this paper, a research trying to find the best model of an echo state network in order to mimic the music of the legendary Wolfgang Amadeus Mozart has been carried out. As it turns out, the best models are the ones that rely on long-term dependencies. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2018 |
| CC license |
|
