| | 1 | **7. Користена литература** |
| | 2 | |
| | 3 | 1. https://en.wikipedia.org/wiki/History_of_music |
| | 4 | 2. https://en.wikipedia.org/wiki/Music_genre |
| | 5 | 3. https://en.wikipedia.org/wiki/Music_theory |
| | 6 | 4. https://www.britannica.com/art/rhythm-music |
| | 7 | 5. https://en.wikipedia.org/wiki/Musical_note |
| | 8 | 6. https://www.eupedia.com/forum/threads/26486-Macedonian-folk-and-traditional-songs |
| | 9 | 7. https://en.wikipedia.org/wiki/Drone_(music) |
| | 10 | 8. https://www.myguidemacedonia.com/travel-articles/traditional-macedonian-folk-dances |
| | 11 | 9. http://folkcloud.com/folk-music-by-country/macedonia |
| | 12 | 10. https://en.wikipedia.org/wiki/Music_of_the_Republic_of_Macedonia |
| | 13 | 11. https://en.wikipedia.org/wiki/Wind_chime#Eastern_and_Southern_Asia |
| | 14 | 12. https://en.wikipedia.org/wiki/Aeolian_harp |
| | 15 | 13. https://en.wikipedia.org/wiki/Suikinkutsu |
| | 16 | 14. https://en.wikipedia.org/wiki/Musikalisches_W%C3%BCrfelspiel |
| | 17 | 15. https://en.wikipedia.org/wiki/Markov_chain |
| | 18 | 16. http://zx.rs/3/Markov-Composer---Using-machine-learning-and-a-Markov-chain-to-compose-music/ |
| | 19 | 17. https://medium.com/artists-and-machine-intelligence/neural-nets-for-generating-music-f46dffac21c0 |
| | 20 | 18. https://en.wikipedia.org/wiki/David_Cope |
| | 21 | 19. http://50years.ucsc.edu/fourth-decade/ |
| | 22 | 20. http://www.indiana.edu/~abcwest/pmwiki/pdf/todd.compmusic.1989.pdf |
| | 23 | 21. https://en.wikipedia.org/wiki/Algorithmic_composition |
| | 24 | 22. http://tones.wolfram.com/generate/ |
| | 25 | 23. http://www.stephenwolfram.com/ |
| | 26 | 24. http://dorienherremans.com/sites/default/files/paper_morpheus_dh_ieee_0.pdf |
| | 27 | 25. https://experiments.withgoogle.com/ai/ai-duet/view/ |
| | 28 | 26. https://www.link.cs.cmu.edu/melody-generator/ |
| | 29 | 27. https://en.wikipedia.org/wiki/Outline_of_machine_learning#Deep_learning |
| | 30 | 28. https://en.wikipedia.org/wiki/Artificial_neural_network#Long_short-term_memory |
| | 31 | 29. https://en.wikipedia.org/wiki/Vanishing_gradient_problem |
| | 32 | 30. https://www.slideshare.net/Simplilearn/recurrent-neural-network-rnn-tutorial-rnn-lstm-tutorial-deep-learning-tutorial-simplilearn?qid=7d46a03c-a2e9-448c-8a37-508f813611f8&v=&b=&from_search=2 |
| | 33 | 31. https://www.slideshare.net/YaochiehHu/rnn-lstm-neural-network-for-sequential-data?qid=250d39b9-dbd1-4e59-adc0-52daa280a7f8&v=&b=&from_search=7 |
| | 34 | 32. https://keras.io/ |
| | 35 | 33. https://deepjazz.io/ |
| | 36 | 34. http://bachbot.com/#/?_k=lwnk0l |
| | 37 | 35. https://irakorshunova.github.io/ |
| | 38 | 36. https://www.tensorflow.org/ |
| | 39 | 37. http://web.mit.edu/music21/ |
| | 40 | 38. https://towardsdatascience.com/how-to-generate-music-using-a-lstm-neural-network-in-keras-68786834d4c5 |
| | 41 | 39. http://www.speech.kth.se/prod/publications/files/1047.pdf |
| | 42 | 40. Antonio Rodà 1 , Emery Schubert 2 , Giovanni De Poli 1 , and Sergio Canazza 1: Toward a musical Turing test for automatic music performance. Dept. of Information Engineering, University of Padova, Italy |
| | 43 | 41. Adrien Ycart and Emmanouil Benetos: A study on lstm networks for polyphonic music sequence modelling. Centre for Digital Music, Queen Mary University of London, UK |
| | 44 | 42. Allen Huang, Raymond Wu: Deep Learning for Music. Department of Computer Science Stanford University |
| | 45 | 43. Thayabaran Kathiresan: Automatic Melody Generation. Kth royal institute of technology School of electrical engineering |
| | 46 | 44. Andrew Pfalz: Generating Audio Using Recurrent Neural Networks. Louisiana State University and Agricultural and Mechanical College |
| | 47 | 45. Jeroen Vranken: Generating Music in Different Genres using Long Short-Term Memory Networks. University of Amsterdam |
| | 48 | 46. Shaoxiong Wang ∗ Yuchen Mo † Changran Hu ‡ Weishu Mo § Xiaolin Hu : Generation of Complex Music using LSTM |
| | 49 | 47. Douglas Eck and Jasmin Lapamle: Learning Musical Structure Directly from Sequences of Music. University of Montreal |
| | 50 | |
