Araştırma Makalesi
Yıl 2025,
Sayı: Yapay zekâ ve sanat özel sayısı, 123 - 146, 22.10.2025
Öz
Bu araştırmanın amacı müzisyenlerin sahne performanslarında (çalgı aleti çalma veya şarkı söyleme) veya yaratıcı faaliyetlerinde (beste yapma, düzenleme vb.) yapay zekâ (YZ) kullanım durumlarının ve YZ ilişkin görüşlerinin incelenmesidir. Araştırmada durum tespitine yönelik bir model esas alınmıştır. Çalışma betimsel türde, model olarak genel tarama modelindedir. Çalışma grubunu 2025 yılında amatör veya profesyonel çalgı çalan veya korolarda şarkı söyleyen ve araştırmacı tarafından ulaşılabilen, araştırmaya katılmayı kabul eden 79 müzisyen oluşturmaktadır. Araştırmada veri toplama aracı olarak, bir ölçme değerlendirme uzmanı ve bir müzikoloji uzmanından görüş alınarak hazırlanan anket uygulanmıştır. Müzisyenlerde YZ kullanımının düşük seviyede olduğu tespit edilmiştir. En düşük YZ kullanım düzeyinin icracılıkta olduğu tespit edilmiştir. En yüksek YZ kullanım oranı ise stüdyo pratiklerinde yer almaktadır. Bu durum, yapay zekânın stüdyo ve müzik teknolojileri alanında görece daha fazla entegre olmaya başladığını göstermektedir. Müzisyenlerde YZ uygulamalarının faydasına yönelik sınırlı düzeyde olumlu tutumlar olduğu, ancak bu tutumların uygulamaya dönüşme oranının oldukça düşük kaldığı görülmektedir.
Anahtar Kelimeler
Kaynakça
- Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/Mind/LIX.236.433
- Ben-Tal, O. (2023). The odd couple. Zenodo (CERN European organization for nuclear research). https://doi.org/10.5281/ZENODO.8331056
- Bevilacqua, F., Schnell, N., ve Fdili Alaoui, S. (2016). Towards a gesture-sound cross-modal analysis. Proceedings of the international conference on new interfaces for musical expression.
- Briot, J. P., Hadjeres, G., Pachet, F. D. (2020). Deep learning techniques for music generation. Springer.
- Buchanan, B. G. (2005). A (very) brief history of artificial intelligence. AI magazine, 26(4). 53-60.
- Briot, J.-P., ve Pachet, F. (2017). Music generation by deep learning – A survey. https://doi.org/10.48550/arXiv.1709.01620
- Chen, Y., Huang, L., Gou, T. (2024). Applications and advances of artificial intelligence in music generation: A review https://doi.org/10.48550/arXiv.2409.03715
- Chuan, C. H., Agres, K., Herremans, D. (2020). From context to concept: Exploring semantic relationships in music with word2vec. Neural computing and applications, 32, 11261–11273. https://doi.org/10.48550/arXiv.1811.12408
- Crevier, D. (1993). AI: The tumultuous search for artificial intelligence. Basicbooks.
- Dong, H. W., Hsiao, W. Y., Yang, L. C., Yang, Y. H. (2018). MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In AAAI conference on artificial intelligence, 34–41.
- Drott, E. A. (2021). Music as a technology of surveillance. Journal of the society for american music, 15(4), 595–615. https://doi.org/10.1017/S1752196318000196
- Felten, E. W., Raj, M., ve Seamans, R. (2021). Occupational, industry, and geographic exposure to artificial intelligence: a novel dataset and its potential uses. Strategic management journal, 42(12), 2195–2217. https://onlinelibrary.wiley.com/doi/pdf/10.1002/smj.3286.
- Felten, E. W., Raj, M., Seamans, R. (2023). Occupational heterogeneity in exposure to generative ai. http://dx.doi.org/10.2139/ssrn.4414065
- Fiebrink, R. (2020). Machine learning education for artists, musicians, and other creative practitioners. ACM Transactions on computing education (TOCE), 20(1), 1–32. https://doi.org/10.1145/3294008
- Frid, E., Gomes, C., ve Jin, Z. (2020). Music creation by example. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3313831.3376739
- Herremans, D., Chew, E. (2016). Music generation with structural constraints: An operations research approach, Presented at the 30th Annual Conference of the Belgian Operational Research (OR) Societyn(ORBEL30), Louvain-La-Neuve.
- Huang, C. A., Vaswani, A., Uszkoreit, J., Shazeer, N., Simon, I., Hawthorne, C., Eck, D. (2019). Music transformer: Generating music with long-term structure. Proceedings of the international conference on learning representations.
- Kaplan, A., ve Haenlein, M. Siri, (2019) Siri in my hand: Who’s the fairest in the land on the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz, 62(1), 15-25. https://doi.org/10.1016/j.bushor.2018.08.004
- Karasar, N. (2019). Bilimsel araştırma yöntemi: Kavramlar ilkeler teknikler (34.baskı). Nobel.
- Maes, P. J., Leman, M., Palmer, C., Wanderley, M. M. (2011). Action-based effects on music perception. Frontiers in psychology, 2, 1–14. https://doi.org/10.3389/fpsyg.2013.01008
- Mccorduck, P. (2004). Machines who think: A personal inquiry into the history and prospects of artificial intelligence. Peters publishing.
- Newquist, H.P. (1994). The brain makers: Genius, ego, and greed in the quest for machines that think. Macmillan.
- Pasquier, P., Eigenfeldt, A., Bown, O., ve Dubnov, S. (2017). An introduction to musical metacreation. In E. R. Miranda (Ed.), Handbook of artificial intelligence for music: Foundations, advanced approaches, and developments (pp. 291–328). Springer.
- Roberts, A., Engel, J., Mann, Y., Gillick, J., Kayacik, C., Nørly, S., Eck, D. (2019). Magenta studio: Augmenting creativity with deep learning in ableton live. In proceedings of the international workshop on musical metacreation.
- Solmaz, B. (2021). Teknolojik gelişmelerin müzik alanında oluşturduğu yeniliklerle ilgili bir değerlendirme. Motif akademi halkbilimi dergisi, 14(34), 666-678. https://doi.org/10.12981/mahder.870604
- Sturm, B. L., Ben-Tal, O., Monaghan, Ú., Collins, N., Herremans, D., Chew, E., Pachet, F. (2018). Machine learning research that matters for music creation: A case study. Journal of New Music Research, 48(1), 36–55. https://doi.org/10.1080/09298215.2018.1515233
Yıl 2025,
Sayı: Yapay zekâ ve sanat özel sayısı, 123 - 146, 22.10.2025
Öz
The aim of this study is to examine the use of Artificial Intelligence (AI) in musicians' stage performances or creative activities and their views on AI. The research is based on a model for situation determination. It is in descriptive type and general survey model as a model. The study group consists of 79 musicians who played amateur or professional instruments or sang in choirs in 2025. It was determined that the level of AI use among musicians was low. The lowest level of AI use was found to be in performance. The highest rate of AI use is found in studio practices. This shows that artificial intelligence is becoming relatively more integrated in the field of studio and music technologies. It is seen that musicians have limited positive attitudes towards the benefits of AI, but the rate of turning these attitudes into practice remains quite low.
Anahtar Kelimeler
Kaynakça
- Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/Mind/LIX.236.433
- Ben-Tal, O. (2023). The odd couple. Zenodo (CERN European organization for nuclear research). https://doi.org/10.5281/ZENODO.8331056
- Bevilacqua, F., Schnell, N., ve Fdili Alaoui, S. (2016). Towards a gesture-sound cross-modal analysis. Proceedings of the international conference on new interfaces for musical expression.
- Briot, J. P., Hadjeres, G., Pachet, F. D. (2020). Deep learning techniques for music generation. Springer.
- Buchanan, B. G. (2005). A (very) brief history of artificial intelligence. AI magazine, 26(4). 53-60.
- Briot, J.-P., ve Pachet, F. (2017). Music generation by deep learning – A survey. https://doi.org/10.48550/arXiv.1709.01620
- Chen, Y., Huang, L., Gou, T. (2024). Applications and advances of artificial intelligence in music generation: A review https://doi.org/10.48550/arXiv.2409.03715
- Chuan, C. H., Agres, K., Herremans, D. (2020). From context to concept: Exploring semantic relationships in music with word2vec. Neural computing and applications, 32, 11261–11273. https://doi.org/10.48550/arXiv.1811.12408
- Crevier, D. (1993). AI: The tumultuous search for artificial intelligence. Basicbooks.
- Dong, H. W., Hsiao, W. Y., Yang, L. C., Yang, Y. H. (2018). MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In AAAI conference on artificial intelligence, 34–41.
- Drott, E. A. (2021). Music as a technology of surveillance. Journal of the society for american music, 15(4), 595–615. https://doi.org/10.1017/S1752196318000196
- Felten, E. W., Raj, M., ve Seamans, R. (2021). Occupational, industry, and geographic exposure to artificial intelligence: a novel dataset and its potential uses. Strategic management journal, 42(12), 2195–2217. https://onlinelibrary.wiley.com/doi/pdf/10.1002/smj.3286.
- Felten, E. W., Raj, M., Seamans, R. (2023). Occupational heterogeneity in exposure to generative ai. http://dx.doi.org/10.2139/ssrn.4414065
- Fiebrink, R. (2020). Machine learning education for artists, musicians, and other creative practitioners. ACM Transactions on computing education (TOCE), 20(1), 1–32. https://doi.org/10.1145/3294008
- Frid, E., Gomes, C., ve Jin, Z. (2020). Music creation by example. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3313831.3376739
- Herremans, D., Chew, E. (2016). Music generation with structural constraints: An operations research approach, Presented at the 30th Annual Conference of the Belgian Operational Research (OR) Societyn(ORBEL30), Louvain-La-Neuve.
- Huang, C. A., Vaswani, A., Uszkoreit, J., Shazeer, N., Simon, I., Hawthorne, C., Eck, D. (2019). Music transformer: Generating music with long-term structure. Proceedings of the international conference on learning representations.
- Kaplan, A., ve Haenlein, M. Siri, (2019) Siri in my hand: Who’s the fairest in the land on the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz, 62(1), 15-25. https://doi.org/10.1016/j.bushor.2018.08.004
- Karasar, N. (2019). Bilimsel araştırma yöntemi: Kavramlar ilkeler teknikler (34.baskı). Nobel.
- Maes, P. J., Leman, M., Palmer, C., Wanderley, M. M. (2011). Action-based effects on music perception. Frontiers in psychology, 2, 1–14. https://doi.org/10.3389/fpsyg.2013.01008
- Mccorduck, P. (2004). Machines who think: A personal inquiry into the history and prospects of artificial intelligence. Peters publishing.
- Newquist, H.P. (1994). The brain makers: Genius, ego, and greed in the quest for machines that think. Macmillan.
- Pasquier, P., Eigenfeldt, A., Bown, O., ve Dubnov, S. (2017). An introduction to musical metacreation. In E. R. Miranda (Ed.), Handbook of artificial intelligence for music: Foundations, advanced approaches, and developments (pp. 291–328). Springer.
- Roberts, A., Engel, J., Mann, Y., Gillick, J., Kayacik, C., Nørly, S., Eck, D. (2019). Magenta studio: Augmenting creativity with deep learning in ableton live. In proceedings of the international workshop on musical metacreation.
- Solmaz, B. (2021). Teknolojik gelişmelerin müzik alanında oluşturduğu yeniliklerle ilgili bir değerlendirme. Motif akademi halkbilimi dergisi, 14(34), 666-678. https://doi.org/10.12981/mahder.870604
- Sturm, B. L., Ben-Tal, O., Monaghan, Ú., Collins, N., Herremans, D., Chew, E., Pachet, F. (2018). Machine learning research that matters for music creation: A case study. Journal of New Music Research, 48(1), 36–55. https://doi.org/10.1080/09298215.2018.1515233
Toplam 26 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Sanat ve Kültür Politikası |
| Bölüm | Makale |
| Yazarlar | |
| Yayımlanma Tarihi | 22 Ekim 2025 |
| Gönderilme Tarihi | 12 Temmuz 2025 |
| Kabul Tarihi | 8 Ekim 2025 |
| Yayımlandığı Sayı | Yıl 2025 Sayı: Yapay zekâ ve sanat özel sayısı |
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YAYINCI | DERGİ KURULLARI | DİZİNLER | ÜCRET POLİTİKASI | İSTATİSTİKLER | MIAR | SHERPA ROMEO