Abstract
Technology has had a remarkable influence on music. As society advances technologically, the music industry does as well. An example that illustrates the use of technology in music is the use of artificial intelligence (AI) as a creative and inspiring tool. Music helps shape emotional responses, creates a rhythm, and comments on the action. It is often a very crucial element to any experience. However, music, like any form of art, is an extremely challenging field to tackle using AI. The amount of information in a musical structure can be overwhelmingly large. If we factor in the different and unpredictable nuances invoked by human imperfection and emotion, it becomes clear why, even though AI excels at handling large amounts of data, generating good music can be very challenging, especially when it comes to Jazz and similarly complex genres.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jones, M.R.: Dynamic pattern structure in music: recent theory and research. Percept. Psychophys. 41, 621–634 (1987)
Analytics Vidhya. https://www.analyticsvidhya.com/blog/2020/01/how-to-perform-automatic-music-generation/. Accessed 9 Mar 2021
UX Planet. https://uxplanet.org/why-fast-matters-a-lot-14c202e352f8. Accessed 9 Mar 2021
Musical U. https://www.musical-u.com/learn/rhythm-tips-for-identifying-music-genres-by-ear/. Accessed 7 Mar 2021
Tensorflow. https://magenta.tensorflow.org/studio. Accessed 7 Mar 2021
MuseNet. https://openai.com/blog/musenet/. Accessed 9 Mar 2021
Oore, S., Simon, I., Dieleman, S., Eck, D., Simonyan, K.: This time with feeling: learning expressive musical performance. Neural Comput. Appl. 32(4), 955–967 (2018). https://doi.org/10.1007/s00521-018-3758-9
Twilio. https://www.twilio.com/blog/training-a-neural-network-on-midi-music-data-with-magenta-and-python/. Accessed 7 Mar 2021
Machine Learning Mastery. https://machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/. Accessed 7 Mar 2021
GitHub Magenta. https://github.com/magenta/note-seq. Accessed 9 Mar 2021
Jeremy Jordan. https://www.jeremyjordan.me/variational-autoencoders/. Accessed 7 Mar 2021
GitHub Magenta. https://github.com/magenta/magenta/tree/master/magenta/models/melody_rnn. Accessed 11 Mar 2021
Britannica Art. https://www.britannica.com/art/monophony. Accessed 11 Mar 2021
Britannica Art. https://www.britannica.com/art/polyphony-music. Accessed 11 Mar 2021
Midi.org Midi Quantization. https://www.midi.org/midi-articles/5-midi-quantization-tips-1. Accessed 11 Mar 2021
Ghrab, A.: The western study of intervals in “Arabic Music,” from the eighteenth century to the cairo congress. World Music 47(3), 55–79 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Alaeddine, M., Tannoury, A. (2021). Artificial Intelligence in Music Composition. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-79150-6_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79149-0
Online ISBN: 978-3-030-79150-6
eBook Packages: Computer ScienceComputer Science (R0)