Muzikos generavimas panaudojant mašininį mokymąsi

• Main Author: Krušna, Ąžuolas
• Other Authors: Lukoševičius, Mantas
• Format: Master Thesis
• Language: Lithuanian; English
• Published: Institutional Repository of Kaunas University of Technology 2020
• Subjects: algorithmic composition; echo state network (ESN); MIDI; recurrent neural network; long short-term memory (LSTM) network; baleen whale
• Online Access: http://ktu.oai.elaba.lt/documents/28872349.pdf; http://ktu.lvb.lt/KTU:ELABAETD28872349&prefLang=en_US

Music has always had a special role among humans. In fact, not only humans. Several of the popular baleen whale species have been observed to be singing. Music has been a great companion to our wars, joys and troubles. An overview on the attempts to compose music with computations is given in this article. 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 thorough analysis of the technologies for music generation is carried out. Moreover, 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 realized. As it turns out, the best models are the ones that rely on long-term dependencies. In the end, insights of using an echo state network for music generation are given.