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Türkiye’de Müzik Ve Yapay Zeka Üzerine Bir Değerlendirme

Year 2025, Volume: 20 Issue: MX Yaratıcı Endüstriler Çalıştayı 2024: Yapay Zeka Çağında Yaratıcı Endüstriler Özel Sayısı, 50 - 70, 24.04.2025

Mehmet Selim Yavuz , Mustafa Kemal Karaosmanoğlu , Mehmet Safa Yeprem , Gülay Karşıcı , Ubeydullah Sezikli

Abstract

Bu makale yapay zeka teknolojisinin genel olarak müzikle, özel olarak Türk müziğiyle ve müzik araştırmalarıyla (incelemeleriyle) nasıl ilişkilendirildiği ile ilgilidir. Kaynak taraması yaparak elde edilen veriler ile şekillenen bu yazıda yapay zeka teknolojisinin çok kısa tarihi, müzikte uygulama alanları; müziğe uygulanmış yapay zeka yöntemleri, teknikleri, algoritmaları; geleneksel makine öğrenmesi ve müzikte derin öğrenme yöntemleri; verinin müzik yapay zeka uygulamalarındaki önemi; yapay zekanın müzik eğitimine ve öğretimine etkileri ele alındı. Bunlara ek olarak, bu yazıda Türkiye’de yapay zeka teknolojisi kullanılarak yapılan birkaç müzik projesinden de kısaca söz edildi.

Keywords

Yapay zeka Türk müziği Makine öğrenmesi Müzik eğitimi Müzik araştırmaları

References

  • Alpkoçak, A., & Gedik, A. C. (2006). Classification of Turkish songs according to makams by using n grams. Proceedings of the 15. Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN). Turkish Symposium on Artificial Intelligence and Neural Networks.
  • Anderson, A., Maystre, L., Anderson, I., Mehrotra, R., & Lalmas, M. (2020). Algorithmic Effects on the Diversity of Consumption on Spotify. Proceedings of The Web Conference 2020, 2155-2165. https://doi.org/10.1145/3366423.3380281
  • Becker, H. S. (1976). Art worlds and social types. American Behavioral Scientist, 19(6), s. 703-718. doi:https://doi.org/10.1177/000276427601900603
  • Boulanger-Lewandowski, N., et al. (2012). Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription. Proceedings of the 29th International Conference on Machine Learning (ICML-12).
  • Bozkurt, B., Gedik, A. C. & Karaosmanolu, M. K. (2009). Türk Müziği için Müzik Bilgi Erişimi: problemler, çözüm önerileri ve araçlar. IEEE 17th Signal Processing and Communications Applications Conference, Antalya, Turkey, 2009, pp. 804-807, doi: 10.1109/SIU.2009.5136518.
  • Börekci, A., & Sevli, O. (2024). A classification study for Turkish folk music makam recognition using machine learning with data augmentation techniques. Neural Computing and Applications, 36(4), 1621-1639. https://doi.org/10.1007/s00521-02309177-6
  • Brittain, B. (2024a). Music labels sue AI companies Suno, UdIO for U.S. copyright infringement. Reuters. https://www.reuters.com/technology/artificial-intelligence/music-labels-sue-ai-companies-suno-udio-us-copyrightinfringement-2024-06-24/
  • Brittain, B. (2024b). Music labels, AI lawsuits create new copyright puzzle for US courts. Reuters. https://www.reuters.com/legal/music-labels-ai-lawsuits-create-new-copyright-puzzle-us-courts-2024-08-03/
  • Bryan-Kinns, N., Zhang, B., Zhao, S., & Banar, B. (2024). Exploring Variational Auto-encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI. Machine Intelligence Research, 21(1), 29-45. https://doi.org/10.1007/s11633023-1457-1
  • Cannam, C., Landone, C., Sandler, M., & Bello, J. P. (2006). The Sonic Visualiser: A Visualisation Platform for Semantic Descriptors from Musical Signals. Easing Access to Sound Archives. Easing Access to Sound Archives.
  • Civit, M., Civit-Masot, J., Cuadrado, F., & Escalona, M. J. (2022). A systematic review of artificial intelligence-based music generation: Sco, applications, and future trends. Expert Systems with Applications, 209, 118190. https://doi.org/10.1016/j.eswa.2022.118190
  • Collins, N. (2011). Live coding of Consequence. In Proceedings of the International Computer Music Conference (ICMC).
  • Correya, A. A., Marcos Fernández, J., Joglar-Ongay, L., Alonso Jiménez, P., Serra, X., & Bogdanov, D. (2021). Audio and music analysis on the web using Essentia.js. Transactions of the International Society for Music Information Retrieval. Transactions of the International Society for Music Information Retrieval. https://doi.org/10.5334/tismir.111
  • David E. Rumelhart, G. E. (1986). Learning representations by back-propagating errors. Nature, 323, s. 533-536. doi:https://doi.org/10.1038/323533a0
  • Fışkın, Ü. (2024). Examination of Ulvi Cemal Erkin’s Piano Concerto with GTTM. Journal for the Interdisciplinary Art and Education, 5(1), Article 1. https://doi.org/10.5281/zenodo.10866655
  • Friconnet, G. (2023). A k-means clustering and histogram-based colorimetric analysis of metal album artworks: The colour palette of metal music. İçinde Metal Music Studies (C. 9, Sayı 1, ss. 77-100). Intellect. https://doi.org/10.1386/mms_00095_1
  • Harris, T. (t.y.). Folk RNN. Geliş tarihi 29 Mart 2024, gönderen https://folkrnn.org/
  • Hash—AI-powered Music Notation Tool. (t.y.). Geliş tarihi 29 Mart 2024, gönderen https://hash-music.com
  • Hewlett, W. B., & Selfridge-Field, E. (Ed.). (2001). The Virtual Score, Volume 12: Representation, Retrieval, Restoration. The MIT Press. https://doi.org/10.7551/mitpress/2058.001.0001
  • İmseytoğlu, D., & Yıldız, S. (2012). Yenidoğan Yoğun Bakım Ünitelerinde Müzik Terapi. İÜFN Hem. Derg, 20(2), 160-165.
  • Good, M. (2001). MusicXML for notation and analysis. The virtual score: representation, retrieval, restoration, 12(113–124), 160.
  • Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. The MIT Press.
  • Jakubowski, K., Eerola, T., Alborno, P., Volpe, G., Camurri, A., & Clayton, M. (2017). Extracting Coarse Body Movements from Video in Music Performance: A Comparison of Automated Computer Vision Techniques with Motion Capture Data. Frontiers in Digital Humanities, 4. https://doi.org/10.3389/fdigh.2017.00009
  • Newell, A., & Simon, H. A. (1956). The Logic Theory Machine A Complex Information Processing System. in IRE Transactions on Information Theory, 2(3), s. 61-79. doi:doi: 10.1109/TIT.1956.1056797
  • Kaliakatsos-Papakostas, M., Floros, A., & Vrahatis, M. N. (2020). Artificial intelligence methods for music generation: A review and future perspectives. Içinde Nature-Inspired Computation and Swarm Intelligence (ss. 217-245). Elsevier. https://doi.org/10.1016/B978-0-12-819714-1.00024-5
  • Kaplan, T. (2024). Probabilistic Models of Rhythmic Expectation & Synchronisation [Doktora Tezi, QMUL]. https://qmro.qmul.ac.uk/xmlui/handle/123456789/94727
  • Karaosmanoğlu, M. K. (2012). A Turkish makam music symbolic database for music information retrieval: SymbTr. Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR). 223-228. Porto, Portugal. http://repositori.upf.edu/handle/10230/25700
  • Karaosmanoğlu, M. K. ve Taşçı, F. (2014). Türk Musikisi İçin Symbtr Sembolik Derlemi Üzerinde Otomatik Ezgi Analiz. Porte Akademik: Müzik ve Dans Araştırmaları Dergisi. Müzikte Kuram. Sayı.10. 98-115.
  • Kong, Q., Li, B., Chen, J., & Wang, Y. (2022). GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music (arXiv:2010.07061; Version 3). arXiv. https://doi.org/10.48550/arXiv.2010.07061
  • Kruspe, A. (2024). More than words: Advancements and challenges in speech recognition for singing (arXiv:2403.09298). arXiv. https://doi.org/10.48550/arXiv.2403.09298
  • Kwon, H.-J., Kim, M.-J., Baek, J.-W., & Chung, K. (2022). Voice Frequency Synthesis using VAW-GAN based Amplitude Scaling for Emotion Transformation. KSII Transactions on Internet and Information Systems (TIIS), 16(2), 713-725. https://doi.org/10.3837/tiis.2022.02.018
  • MIDI 1.0 – MIDI.org. (t.y.). Erişilme tarihi 29 Mart 2024, adres https://midi.org/midi-1-0 Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
  • Music Ai. (t.y.). Erişilme tarihi 29 Mart 2024, adres https://meetyourai.github.io/MusicAI/
  • Neutron 4. (t.y.). Erişilme tarihi 29 Mart 2024, adres https://www.izotope.com/en/products/neutron.html
  • Quinlan, J. (1986, March). Induction of decision trees. Centre for Advanced Computing Sciences, New South Wales Institute of Technology, Sydney. Boston: Kluwer Academic Publishers. doi:https://doi.org/10.1007/BF00116251
  • Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 13451359.
  • Parlak, İ. H. (2021). Derin öğrenme teknikleri kullanılarak Türk makam müziği bestelenmesi [Doktora Tezi, Dokuz Eylül Üniversitesi]. https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp
  • Parlak, İ. H., Çebi Y., Işıkhan C. & Birant D. (2021). Deep Learning for Turkish Makam Music Composition. Turkish Journal of Electrical Engineering and Computer Sciences, 29(7), pp.3107-3118. doi:10.3906/elk-2101-44 pausetv (Direktör).
  • (2024). Cem Karaca galaya hologram olarak katıldı. https://www.youtube.com/watch?v=xq8lw6bZCmU
  • RuMind Music Meditation App. (t.y.). Erişilme tarihi 23 Mayıs 2024, adres https://www.rumindapp.com/
  • Semantic Scholar | About Us. (t.y.). Erişilme tarihi 23 Mayıs 2024, adres https://www.semanticscholar.org/about
  • Wang, A. L.-C. (2003). An Industrial Strength Audio Search Algorithm. Proceedings of the 4th International Society for Music Information Retrieval Conference (ISMIR 2003), Baltimore, Maryland (USA), 26-30 October 2003, 7–13. https://doi.org/10.1109/IITAW.2009.110
  • Suno AI. (t.y.). Erişilme tarihi 29 Mart 2024, adres https://www.suno.ai/
  • Raffel, C. (2016). Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching. PhD Thesis.
  • Weiss, S. M., & Indurkhya, N. (1995, December 1). Rule-based Machine Learning Methods for Functional Prediction. Journal of Artificial Intelligence Research, 3, s. 383-403. doi:https://doi.org/10.1613/jair.199
  • Wen, Z., Chen, A., Zhou, G., Yi, J., & Peng, W. (2024). Parallel attention of representation global time–frequency correlation for music genre classification. Multimedia Tools and Applications, 83(4), 10211-10231. https://doi.org/10.1007/s11042-023-16024-2
  • Widmer, G. (2003). Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries. Artificial Intelligence, 146(2), s. 129-148. doi:https://doi.org/10.1016/S0004-3702(03)00016-X
  • Wijaya, N. N., Setiadi, D. R. I. M., & Muslikh, A. R. (2024). Music-Genre Classification using Bidirectional Long Short-Term Memory and Mel-Frequency Cepstral Coefficients. Journal of Computing Theories and Applications, 2(1), Article 1. https://doi.org/10.62411/jcta.9655
  • Thickstun, J., Harchaoui, Z., & Kakade, S. (2017). Learning Features of Music from Scratch (arXiv:1611.09827). arXiv. http://arxiv.org/abs/1611.09827
  • Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293-302.
  • Yıldız, Ş., Güvener, D., & Güray, C. (2024). Adakale Türkülerinin Yeniden Yapılandırılması için bir Algoritmik Kompozisyon Yöntemi Arayışı. Sahne ve Müzik Eğitim - Araştırma e-Dergisi, 10(18), 80-100.
  • Yücel, İ. E. (2022). MIXPREP: Machine Learning-Based Multitrack Mix Preparation Assistant [Doktora Tezi]. İstanbul Teknik Üniversitesi.

AN OVERVIEW OF ARTIFICIAL INTELLIGENCE IN THE CONTEXT OF MUSIC AND TÜRKİYE

Year 2025, Volume: 20 Issue: MX Yaratıcı Endüstriler Çalıştayı 2024: Yapay Zeka Çağında Yaratıcı Endüstriler Özel Sayısı, 50 - 70, 24.04.2025

Mehmet Selim Yavuz , Mustafa Kemal Karaosmanoğlu , Mehmet Safa Yeprem , Gülay Karşıcı , Ubeydullah Sezikli

Abstract

This article concerns the relationship between artificial intelligence technology and music in general, with a particular focus on Turkish music and music research (studies). Shaped by data obtained from a literature review, this paper explores the brief history of artificial intelligence technology, its application areas in music, artificial intelligence methods, techniques, and algorithms applied to music, traditional machine learning and deep learning methods in music, the importance of data in music artificial intelligence applications, and the impacts of artificial intelligence on music education and teaching. In addition, this paper briefly discusses several music projects in Turkey that have been conducted using artificial intelligence technology.

Keywords

Artificial intelligence Classical Turkish music Machine learning Music education Music research

References

  • Alpkoçak, A., & Gedik, A. C. (2006). Classification of Turkish songs according to makams by using n grams. Proceedings of the 15. Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN). Turkish Symposium on Artificial Intelligence and Neural Networks.
  • Anderson, A., Maystre, L., Anderson, I., Mehrotra, R., & Lalmas, M. (2020). Algorithmic Effects on the Diversity of Consumption on Spotify. Proceedings of The Web Conference 2020, 2155-2165. https://doi.org/10.1145/3366423.3380281
  • Becker, H. S. (1976). Art worlds and social types. American Behavioral Scientist, 19(6), s. 703-718. doi:https://doi.org/10.1177/000276427601900603
  • Boulanger-Lewandowski, N., et al. (2012). Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription. Proceedings of the 29th International Conference on Machine Learning (ICML-12).
  • Bozkurt, B., Gedik, A. C. & Karaosmanolu, M. K. (2009). Türk Müziği için Müzik Bilgi Erişimi: problemler, çözüm önerileri ve araçlar. IEEE 17th Signal Processing and Communications Applications Conference, Antalya, Turkey, 2009, pp. 804-807, doi: 10.1109/SIU.2009.5136518.
  • Börekci, A., & Sevli, O. (2024). A classification study for Turkish folk music makam recognition using machine learning with data augmentation techniques. Neural Computing and Applications, 36(4), 1621-1639. https://doi.org/10.1007/s00521-02309177-6
  • Brittain, B. (2024a). Music labels sue AI companies Suno, UdIO for U.S. copyright infringement. Reuters. https://www.reuters.com/technology/artificial-intelligence/music-labels-sue-ai-companies-suno-udio-us-copyrightinfringement-2024-06-24/
  • Brittain, B. (2024b). Music labels, AI lawsuits create new copyright puzzle for US courts. Reuters. https://www.reuters.com/legal/music-labels-ai-lawsuits-create-new-copyright-puzzle-us-courts-2024-08-03/
  • Bryan-Kinns, N., Zhang, B., Zhao, S., & Banar, B. (2024). Exploring Variational Auto-encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI. Machine Intelligence Research, 21(1), 29-45. https://doi.org/10.1007/s11633023-1457-1
  • Cannam, C., Landone, C., Sandler, M., & Bello, J. P. (2006). The Sonic Visualiser: A Visualisation Platform for Semantic Descriptors from Musical Signals. Easing Access to Sound Archives. Easing Access to Sound Archives.
  • Civit, M., Civit-Masot, J., Cuadrado, F., & Escalona, M. J. (2022). A systematic review of artificial intelligence-based music generation: Sco, applications, and future trends. Expert Systems with Applications, 209, 118190. https://doi.org/10.1016/j.eswa.2022.118190
  • Collins, N. (2011). Live coding of Consequence. In Proceedings of the International Computer Music Conference (ICMC).
  • Correya, A. A., Marcos Fernández, J., Joglar-Ongay, L., Alonso Jiménez, P., Serra, X., & Bogdanov, D. (2021). Audio and music analysis on the web using Essentia.js. Transactions of the International Society for Music Information Retrieval. Transactions of the International Society for Music Information Retrieval. https://doi.org/10.5334/tismir.111
  • David E. Rumelhart, G. E. (1986). Learning representations by back-propagating errors. Nature, 323, s. 533-536. doi:https://doi.org/10.1038/323533a0
  • Fışkın, Ü. (2024). Examination of Ulvi Cemal Erkin’s Piano Concerto with GTTM. Journal for the Interdisciplinary Art and Education, 5(1), Article 1. https://doi.org/10.5281/zenodo.10866655
  • Friconnet, G. (2023). A k-means clustering and histogram-based colorimetric analysis of metal album artworks: The colour palette of metal music. İçinde Metal Music Studies (C. 9, Sayı 1, ss. 77-100). Intellect. https://doi.org/10.1386/mms_00095_1
  • Harris, T. (t.y.). Folk RNN. Geliş tarihi 29 Mart 2024, gönderen https://folkrnn.org/
  • Hash—AI-powered Music Notation Tool. (t.y.). Geliş tarihi 29 Mart 2024, gönderen https://hash-music.com
  • Hewlett, W. B., & Selfridge-Field, E. (Ed.). (2001). The Virtual Score, Volume 12: Representation, Retrieval, Restoration. The MIT Press. https://doi.org/10.7551/mitpress/2058.001.0001
  • İmseytoğlu, D., & Yıldız, S. (2012). Yenidoğan Yoğun Bakım Ünitelerinde Müzik Terapi. İÜFN Hem. Derg, 20(2), 160-165.
  • Good, M. (2001). MusicXML for notation and analysis. The virtual score: representation, retrieval, restoration, 12(113–124), 160.
  • Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. The MIT Press.
  • Jakubowski, K., Eerola, T., Alborno, P., Volpe, G., Camurri, A., & Clayton, M. (2017). Extracting Coarse Body Movements from Video in Music Performance: A Comparison of Automated Computer Vision Techniques with Motion Capture Data. Frontiers in Digital Humanities, 4. https://doi.org/10.3389/fdigh.2017.00009
  • Newell, A., & Simon, H. A. (1956). The Logic Theory Machine A Complex Information Processing System. in IRE Transactions on Information Theory, 2(3), s. 61-79. doi:doi: 10.1109/TIT.1956.1056797
  • Kaliakatsos-Papakostas, M., Floros, A., & Vrahatis, M. N. (2020). Artificial intelligence methods for music generation: A review and future perspectives. Içinde Nature-Inspired Computation and Swarm Intelligence (ss. 217-245). Elsevier. https://doi.org/10.1016/B978-0-12-819714-1.00024-5
  • Kaplan, T. (2024). Probabilistic Models of Rhythmic Expectation & Synchronisation [Doktora Tezi, QMUL]. https://qmro.qmul.ac.uk/xmlui/handle/123456789/94727
  • Karaosmanoğlu, M. K. (2012). A Turkish makam music symbolic database for music information retrieval: SymbTr. Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR). 223-228. Porto, Portugal. http://repositori.upf.edu/handle/10230/25700
  • Karaosmanoğlu, M. K. ve Taşçı, F. (2014). Türk Musikisi İçin Symbtr Sembolik Derlemi Üzerinde Otomatik Ezgi Analiz. Porte Akademik: Müzik ve Dans Araştırmaları Dergisi. Müzikte Kuram. Sayı.10. 98-115.
  • Kong, Q., Li, B., Chen, J., & Wang, Y. (2022). GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music (arXiv:2010.07061; Version 3). arXiv. https://doi.org/10.48550/arXiv.2010.07061
  • Kruspe, A. (2024). More than words: Advancements and challenges in speech recognition for singing (arXiv:2403.09298). arXiv. https://doi.org/10.48550/arXiv.2403.09298
  • Kwon, H.-J., Kim, M.-J., Baek, J.-W., & Chung, K. (2022). Voice Frequency Synthesis using VAW-GAN based Amplitude Scaling for Emotion Transformation. KSII Transactions on Internet and Information Systems (TIIS), 16(2), 713-725. https://doi.org/10.3837/tiis.2022.02.018
  • MIDI 1.0 – MIDI.org. (t.y.). Erişilme tarihi 29 Mart 2024, adres https://midi.org/midi-1-0 Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
  • Music Ai. (t.y.). Erişilme tarihi 29 Mart 2024, adres https://meetyourai.github.io/MusicAI/
  • Neutron 4. (t.y.). Erişilme tarihi 29 Mart 2024, adres https://www.izotope.com/en/products/neutron.html
  • Quinlan, J. (1986, March). Induction of decision trees. Centre for Advanced Computing Sciences, New South Wales Institute of Technology, Sydney. Boston: Kluwer Academic Publishers. doi:https://doi.org/10.1007/BF00116251
  • Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 13451359.
  • Parlak, İ. H. (2021). Derin öğrenme teknikleri kullanılarak Türk makam müziği bestelenmesi [Doktora Tezi, Dokuz Eylül Üniversitesi]. https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp
  • Parlak, İ. H., Çebi Y., Işıkhan C. & Birant D. (2021). Deep Learning for Turkish Makam Music Composition. Turkish Journal of Electrical Engineering and Computer Sciences, 29(7), pp.3107-3118. doi:10.3906/elk-2101-44 pausetv (Direktör).
  • (2024). Cem Karaca galaya hologram olarak katıldı. https://www.youtube.com/watch?v=xq8lw6bZCmU
  • RuMind Music Meditation App. (t.y.). Erişilme tarihi 23 Mayıs 2024, adres https://www.rumindapp.com/
  • Semantic Scholar | About Us. (t.y.). Erişilme tarihi 23 Mayıs 2024, adres https://www.semanticscholar.org/about
  • Wang, A. L.-C. (2003). An Industrial Strength Audio Search Algorithm. Proceedings of the 4th International Society for Music Information Retrieval Conference (ISMIR 2003), Baltimore, Maryland (USA), 26-30 October 2003, 7–13. https://doi.org/10.1109/IITAW.2009.110
  • Suno AI. (t.y.). Erişilme tarihi 29 Mart 2024, adres https://www.suno.ai/
  • Raffel, C. (2016). Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching. PhD Thesis.
  • Weiss, S. M., & Indurkhya, N. (1995, December 1). Rule-based Machine Learning Methods for Functional Prediction. Journal of Artificial Intelligence Research, 3, s. 383-403. doi:https://doi.org/10.1613/jair.199
  • Wen, Z., Chen, A., Zhou, G., Yi, J., & Peng, W. (2024). Parallel attention of representation global time–frequency correlation for music genre classification. Multimedia Tools and Applications, 83(4), 10211-10231. https://doi.org/10.1007/s11042-023-16024-2
  • Widmer, G. (2003). Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries. Artificial Intelligence, 146(2), s. 129-148. doi:https://doi.org/10.1016/S0004-3702(03)00016-X
  • Wijaya, N. N., Setiadi, D. R. I. M., & Muslikh, A. R. (2024). Music-Genre Classification using Bidirectional Long Short-Term Memory and Mel-Frequency Cepstral Coefficients. Journal of Computing Theories and Applications, 2(1), Article 1. https://doi.org/10.62411/jcta.9655
  • Thickstun, J., Harchaoui, Z., & Kakade, S. (2017). Learning Features of Music from Scratch (arXiv:1611.09827). arXiv. http://arxiv.org/abs/1611.09827
  • Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293-302.
  • Yıldız, Ş., Güvener, D., & Güray, C. (2024). Adakale Türkülerinin Yeniden Yapılandırılması için bir Algoritmik Kompozisyon Yöntemi Arayışı. Sahne ve Müzik Eğitim - Araştırma e-Dergisi, 10(18), 80-100.
  • Yücel, İ. E. (2022). MIXPREP: Machine Learning-Based Multitrack Mix Preparation Assistant [Doktora Tezi]. İstanbul Teknik Üniversitesi.
There are 52 citations in total.

Details

Primary Language Turkish
Subjects Music Education, Music Technology and Recording, Musicology and Ethnomusicology, Music (Other)
Journal Section Makale Başvuru
Authors

Mehmet Selim Yavuz 0000-0002-3111-6917

Mustafa Kemal Karaosmanoğlu 0000-0003-4534-7182

Mehmet Safa Yeprem 0000-0003-2395-9380

Gülay Karşıcı 0000-0002-5322-6201

Ubeydullah Sezikli 0000-0001-7312-6737

Early Pub Date April 24, 2025
Publication Date April 24, 2025
Submission Date June 11, 2024
Acceptance Date September 30, 2024
Published in Issue Year 2025 Volume: 20 Issue: MX Yaratıcı Endüstriler Çalıştayı 2024: Yapay Zeka Çağında Yaratıcı Endüstriler Özel Sayısı

Cite

APA Yavuz, M. S., Karaosmanoğlu, M. K., Yeprem, M. S., Karşıcı, G., et al. (2025). Türkiye’de Müzik Ve Yapay Zeka Üzerine Bir Değerlendirme. Öneri Dergisi, 20(MX Yaratıcı Endüstriler Çalıştayı 2024: Yapay Zeka Çağında Yaratıcı Endüstriler Özel Sayısı), 50-70.

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Öneri

Marmara UniversityInstitute of Social Sciences

Göztepe Kampüsü Enstitüler Binası Kat:5 34722  Kadıköy/İstanbul

e-ISSN: 2147-5377