Araştırma Makalesi
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Yıl 2025, Cilt: 8 Sayı: 4, 4105 - 4139, 31.12.2025
https://doi.org/10.51576/ymd.1815785

Öz

Kaynakça

  • Acar, A. K. (2025). Yeni medya teknolojilerinde müzisyen kimliğinin dönüşümü: Yapay zekâ, metaverse ve dijital dönüşüm kavramları üzerine bibliyometrik analiz. Online Journal of Music Sciences, 10(2), 219-234. https://doi.org/10.31811/ojomus.1624090
  • Ajiboye, S. A. (2024). Harmonizing spaces: Investigating the intersection of sound and architectural design. Studies in Art and Architecture, 3(3), 46–56.
  • Aria, M. and Cuccurullo, C. (2017). Bibliometrix: An R tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  • Barbosa, A. and Tsang, T. (2017). Sounding architecture: Inter-disciplinary studio at HKU. Proceedings of the 2017 International Conference on New Interfaces for Musical Expression (NIME), Blacksburg, VA.
  • Bevilacqua, A. and Iannace, G. (2023). Acoustic study of the Roman theatre of Pompeii: Comparison between existing condition and future installation of two parametric acoustic shells. The Journal of the Acoustical Society of America, 154(4), 2211–2226. https://doi.org/10.1121/10.0021312
  • Bisig, D., Schacher, J., and Neukom, M. (2011). Flowspace – A hybrid ecosystem. Proceedings of the International Conference on New Interfaces for Musical Expression (NIME 2011), 260-263.
  • Boes, M., Filipan, K., De Coensel, B. and Botteldooren, D. (2018). Machine listening for park soundscape quality assessment. Acta Acustica united with Acustica, 104(1), 121–130.
  • Bucaro, J. A., Waters, Z. J., Houston, B. H., Simpson, H. J., Sarkissian, A., Dey, S., and Yoder, T. J. (2012). Acoustic identification of buried underwater unexploded ordnance using a numerically trained classifier (L). The Journal of the Acoustical Society of America, 132(6), 3614–3617. https://doi.org/10.1121/1.4763997
  • Büyükkaragöz, T. (2025). Sanatta yapay zekâ ve bir öncü: Refik Anadol’un veri estetiği. International Social Sciences Studies Journal, 11(8), 1379–1394.
  • Caramiaux, B., Françoise, J., Schnell, N. and Bevilacqua, F. (2014). Mapping through listening. Computer Music Journal, 38(3), 34–48.
  • Carnovalini, F. and Rodà, A. (2020). Computational creativity and music generation systems: An introduction to the state of the art. Frontiers in Artificial Intelligence, 3, 14. https://doi.org/10.3389/frai.2020.00014
  • Cengizoglu, F. P. (2024). Bibliometric analysis on artificial intelligence aided architectural design. Journal of Design Studio, 6(2), 231-245. https://doi.org/10.46474/jds.1525949
  • Chen, Y., Huang, L. and Gou, T. (2024). Applications and Advances of Artificial Intelligence in Music Generation: A Review. ArXiv. https://arxiv.org/abs/2409.03715
  • D’Alessandro, N., Calderon, R. and Müller, S. (2011). ROOM#81 – Agent-based instrument for experiencing architectural and vocal cues. Proceedings of the International Conference on New Interfaces for Musical Expression (NIME 2011), 132–135.
  • Delikanlı, B. and Çağdaş, G. (2021). Hesaplamalı tasarımda disiplinler ötesi kavramlar ve eğitime yansımaları. Mimarlıkta Sayısal Tasarım XV. Ulusal Sempozyumu (28–29 Haziran 2021, İstanbul) (ss. 93–104). İstanbul: İTÜ Yayınevi.
  • Dziwis, D., von Coler, H. and Pörschmann, C. (2023). Orchestra: a toolbox for live music performances in a Web-Based metaverse. Journal of the Audio Engineering Society, 71(11), 802–812. https://doi.org/10.17743/jaes.2022.0096
  • Einbond, A., Bresson, J., Schwarz, D. and Carpentier, T. (2021). Instrumental radiation patterns as models for corpus-based spatial sound synthesis: Cosmologies for piano and 3D electronics. In Proceedings of the International Computer Music Conference 2021 (pp. 148–153).
  • Engel, J., Hantrakul, L., Gu, C. and Roberts, A. (2020). DDSP: Differentiable digital signal processing. arXiv. https://doi.org/10.48550/arxiv.2001.04643
  • Glass, A. (2020). An algorithmic design method and a three-dimensional active simulation program for acoustically functional halls. (Unpublished doctoral dissertation). Mimar Sinan Fine Arts University, İstanbul, Türkiye.
  • Knees, P., Schedl, M. and Goto, M. (2020). Intelligent user interfaces for music discovery. Transactions of the International Society for Music Information Retrieval, 3(1), 165–179. https://doi.org/10.5334/tismir.60
  • Liu, D., Shahid, Z., Tung, Y., Muliana, A., Ham, Y., Kalantar, N., Chaspari, T., Green, E. and Hubbard, J. E. (2023). Tunable acoustic properties in reconfigurable kerf structures. Journal of Architectural Engineering, 29(3). https://doi.org/10.1061/jaeied.aeeng-1539
  • Lluís, F., Martínez-Nuevo, P., Møller, M. B. and Shepstone, S. E. (2020). Sound field reconstruction in rooms: Inpainting meets super-resolution. The Journal of the Acoustical Society of America, 148(2), 649–659.
  • Oldoni, D., De Coensel, B., Boes, M., Rademaker, M., De Baets, B. and Botteldooren, D. (2013). A computational model of auditory attention for use in soundscape research. The Journal of the Acoustical Society of America, 134(1), 852–861.
  • Özdemir, M. and Arslan-Selçuk, S. (2021). Mimarlıkta makine öğrenmesi: Bibliyometrik bir analiz. Online Journal of Art and Design, 9(4), 194-207.
  • Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
  • Pošćić, A. and Kreković, G. (2020b). On the Human Role in Generative Art: A Case Study of AI-driven Live Coding. Journal of Science and Technology of the Arts, 12(3), 45–62. https://doi.org/10.34632/jsta.2020.9488
  • Schedl, M. and Flexer, A. (2012). Putting the User in the Center of Music Information Retrieval. International Society for Music Information Retrieval Conference.
  • Stefani, D. and Turchet, L. (2023). Real-Time Embedded Deep Learning on Elk Audio OS. IEEE, 1–10. https://doi.org/10.1109/ieeeconf59510.2023.10335204
  • Taşcı, M. H. and Öztürk, B. (2021). From sound and volume to music and space: The relationship of music and concert halls at the Istanbul Music Festival, Hagia Irene Museum and Lütfi Kırdar Hall in 3rd International Hagia Sophia Multidisciplinary Scientific Research Congress.
  • Thorogood, M., Fan, J. and Pasquier, P. (2019). A framework for computer-assisted sound design systems supported by modelling affective and perceptual properties of soundscape. Journal of New Music Research, 48(3), 264–280.
  • Van Eck, N. J. and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
  • Wang, F., Chen, Z. and Wu, C. (2019). Applicability of artificial neural network to estimate sound transmission loss of ultrafine glass fiber felts. Acta Acustica United With Acustica, 105(4), 650–656. https://doi.org/10.3813/aaa.919345
  • Wang, L. and Cavallaro, A. (2022). Deep-learning-assisted sound source localization from a flying drone. IEEE Sensors Journal, 22(21), 20828-20838.
  • Whalley, I. (2015). Developing Telematic Electroacoustic Music: Complex networks, machine intelligence and affective data stream sonification. Organised Sound, 20(1), 90–98. doi:10.1017/S1355771814000478

ALGORİTMALARDAN AKUSTİĞE: MÜZİK VE MİMARİ TASARIM KESİŞİMİNDE YAPAY ZEKÂ TABANLI YAKLAŞIMLARIN BİLİMSEL HARİTALAMASI

Yıl 2025, Cilt: 8 Sayı: 4, 4105 - 4139, 31.12.2025
https://doi.org/10.51576/ymd.1815785

Öz

Bu çalışma, yapay zekâ, mimarlık ve müzik kesişimindeki bilimsel üretimi bibliyometrik ve sistematik bir yaklaşımla haritalamaktadır. 11 Ekim 2025 tarihinde Web of Science Core Collection veri tabanında gerçekleştirilen kapsamlı tarama sonucunda, 2015-2025 dönemine ait 6.168 kayıt elde edilmiştir. Bibliometrix ve VOSviewer yazılımları kullanılarak tanımlayıcı metrikler, atıf ve işbirliği ağları ile anahtar kelime kümeleri analiz edilmiştir. PRISMA protokolü doğrultusunda belirlenen dahil edilme ölçütlerini karşılayan 21 yayın sistematik incelemeye dahil edilmiştir. Bulgular, yayın hacminde istikrarlı bir artış, yüksek ortak yazarlık oranı ve Çin ile ABD merkezli bir üretim coğrafyasını ortaya koymaktadır. Kaynak dağılımı mühendislik ve akustik dergilerinde yoğunlaşmakta; kavramsal ağın merkezinde derin öğrenme ve makine öğrenmesi yer almaktadır. Tematik olarak üç ana eksen belirlenmiştir: parametrik ve üretken akustik tasarım, ses peyzajı ve kentsel çevre, yapay zekâ destekli enstalasyonlar ve medya mimarlığı. Çalışma, alanın erken keşif evresinden uygulama odaklı bir olgunlaşma sürecine geçtiğini ve yapay zekânın mekânsal işitsel deneyimi dönüştüren bir tasarım aktörüne dönüştüğünü göstermektedir. Ayrıca, gelecekteki araştırmaların bu üç tematik eksende deneysel çalışmalarla derinleştirilmesi ve çoklu veri tabanlarına dayalı geniş kapsamlı bibliyometrik analizlerle desteklenmesi önerilmektedir.

Kaynakça

  • Acar, A. K. (2025). Yeni medya teknolojilerinde müzisyen kimliğinin dönüşümü: Yapay zekâ, metaverse ve dijital dönüşüm kavramları üzerine bibliyometrik analiz. Online Journal of Music Sciences, 10(2), 219-234. https://doi.org/10.31811/ojomus.1624090
  • Ajiboye, S. A. (2024). Harmonizing spaces: Investigating the intersection of sound and architectural design. Studies in Art and Architecture, 3(3), 46–56.
  • Aria, M. and Cuccurullo, C. (2017). Bibliometrix: An R tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  • Barbosa, A. and Tsang, T. (2017). Sounding architecture: Inter-disciplinary studio at HKU. Proceedings of the 2017 International Conference on New Interfaces for Musical Expression (NIME), Blacksburg, VA.
  • Bevilacqua, A. and Iannace, G. (2023). Acoustic study of the Roman theatre of Pompeii: Comparison between existing condition and future installation of two parametric acoustic shells. The Journal of the Acoustical Society of America, 154(4), 2211–2226. https://doi.org/10.1121/10.0021312
  • Bisig, D., Schacher, J., and Neukom, M. (2011). Flowspace – A hybrid ecosystem. Proceedings of the International Conference on New Interfaces for Musical Expression (NIME 2011), 260-263.
  • Boes, M., Filipan, K., De Coensel, B. and Botteldooren, D. (2018). Machine listening for park soundscape quality assessment. Acta Acustica united with Acustica, 104(1), 121–130.
  • Bucaro, J. A., Waters, Z. J., Houston, B. H., Simpson, H. J., Sarkissian, A., Dey, S., and Yoder, T. J. (2012). Acoustic identification of buried underwater unexploded ordnance using a numerically trained classifier (L). The Journal of the Acoustical Society of America, 132(6), 3614–3617. https://doi.org/10.1121/1.4763997
  • Büyükkaragöz, T. (2025). Sanatta yapay zekâ ve bir öncü: Refik Anadol’un veri estetiği. International Social Sciences Studies Journal, 11(8), 1379–1394.
  • Caramiaux, B., Françoise, J., Schnell, N. and Bevilacqua, F. (2014). Mapping through listening. Computer Music Journal, 38(3), 34–48.
  • Carnovalini, F. and Rodà, A. (2020). Computational creativity and music generation systems: An introduction to the state of the art. Frontiers in Artificial Intelligence, 3, 14. https://doi.org/10.3389/frai.2020.00014
  • Cengizoglu, F. P. (2024). Bibliometric analysis on artificial intelligence aided architectural design. Journal of Design Studio, 6(2), 231-245. https://doi.org/10.46474/jds.1525949
  • Chen, Y., Huang, L. and Gou, T. (2024). Applications and Advances of Artificial Intelligence in Music Generation: A Review. ArXiv. https://arxiv.org/abs/2409.03715
  • D’Alessandro, N., Calderon, R. and Müller, S. (2011). ROOM#81 – Agent-based instrument for experiencing architectural and vocal cues. Proceedings of the International Conference on New Interfaces for Musical Expression (NIME 2011), 132–135.
  • Delikanlı, B. and Çağdaş, G. (2021). Hesaplamalı tasarımda disiplinler ötesi kavramlar ve eğitime yansımaları. Mimarlıkta Sayısal Tasarım XV. Ulusal Sempozyumu (28–29 Haziran 2021, İstanbul) (ss. 93–104). İstanbul: İTÜ Yayınevi.
  • Dziwis, D., von Coler, H. and Pörschmann, C. (2023). Orchestra: a toolbox for live music performances in a Web-Based metaverse. Journal of the Audio Engineering Society, 71(11), 802–812. https://doi.org/10.17743/jaes.2022.0096
  • Einbond, A., Bresson, J., Schwarz, D. and Carpentier, T. (2021). Instrumental radiation patterns as models for corpus-based spatial sound synthesis: Cosmologies for piano and 3D electronics. In Proceedings of the International Computer Music Conference 2021 (pp. 148–153).
  • Engel, J., Hantrakul, L., Gu, C. and Roberts, A. (2020). DDSP: Differentiable digital signal processing. arXiv. https://doi.org/10.48550/arxiv.2001.04643
  • Glass, A. (2020). An algorithmic design method and a three-dimensional active simulation program for acoustically functional halls. (Unpublished doctoral dissertation). Mimar Sinan Fine Arts University, İstanbul, Türkiye.
  • Knees, P., Schedl, M. and Goto, M. (2020). Intelligent user interfaces for music discovery. Transactions of the International Society for Music Information Retrieval, 3(1), 165–179. https://doi.org/10.5334/tismir.60
  • Liu, D., Shahid, Z., Tung, Y., Muliana, A., Ham, Y., Kalantar, N., Chaspari, T., Green, E. and Hubbard, J. E. (2023). Tunable acoustic properties in reconfigurable kerf structures. Journal of Architectural Engineering, 29(3). https://doi.org/10.1061/jaeied.aeeng-1539
  • Lluís, F., Martínez-Nuevo, P., Møller, M. B. and Shepstone, S. E. (2020). Sound field reconstruction in rooms: Inpainting meets super-resolution. The Journal of the Acoustical Society of America, 148(2), 649–659.
  • Oldoni, D., De Coensel, B., Boes, M., Rademaker, M., De Baets, B. and Botteldooren, D. (2013). A computational model of auditory attention for use in soundscape research. The Journal of the Acoustical Society of America, 134(1), 852–861.
  • Özdemir, M. and Arslan-Selçuk, S. (2021). Mimarlıkta makine öğrenmesi: Bibliyometrik bir analiz. Online Journal of Art and Design, 9(4), 194-207.
  • Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
  • Pošćić, A. and Kreković, G. (2020b). On the Human Role in Generative Art: A Case Study of AI-driven Live Coding. Journal of Science and Technology of the Arts, 12(3), 45–62. https://doi.org/10.34632/jsta.2020.9488
  • Schedl, M. and Flexer, A. (2012). Putting the User in the Center of Music Information Retrieval. International Society for Music Information Retrieval Conference.
  • Stefani, D. and Turchet, L. (2023). Real-Time Embedded Deep Learning on Elk Audio OS. IEEE, 1–10. https://doi.org/10.1109/ieeeconf59510.2023.10335204
  • Taşcı, M. H. and Öztürk, B. (2021). From sound and volume to music and space: The relationship of music and concert halls at the Istanbul Music Festival, Hagia Irene Museum and Lütfi Kırdar Hall in 3rd International Hagia Sophia Multidisciplinary Scientific Research Congress.
  • Thorogood, M., Fan, J. and Pasquier, P. (2019). A framework for computer-assisted sound design systems supported by modelling affective and perceptual properties of soundscape. Journal of New Music Research, 48(3), 264–280.
  • Van Eck, N. J. and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
  • Wang, F., Chen, Z. and Wu, C. (2019). Applicability of artificial neural network to estimate sound transmission loss of ultrafine glass fiber felts. Acta Acustica United With Acustica, 105(4), 650–656. https://doi.org/10.3813/aaa.919345
  • Wang, L. and Cavallaro, A. (2022). Deep-learning-assisted sound source localization from a flying drone. IEEE Sensors Journal, 22(21), 20828-20838.
  • Whalley, I. (2015). Developing Telematic Electroacoustic Music: Complex networks, machine intelligence and affective data stream sonification. Organised Sound, 20(1), 90–98. doi:10.1017/S1355771814000478

FROM ALGORITHMS TO ACOUSTICS: A SCIENTIFIC MAPPING OF AI-BASED APPROACHES IN MUSIC AND ARCHITECTURAL DESIGN INTERSECTION

Yıl 2025, Cilt: 8 Sayı: 4, 4105 - 4139, 31.12.2025
https://doi.org/10.51576/ymd.1815785

Öz

This study maps scientific output at the intersection of artificial intelligence, architecture, and music using a bibliometric and systematic approach. A comprehensive search conducted on the Web of Science Core Collection on October 11, 2025, yielded 6,168 records from the period 2015-2025. Descriptive metrics, citation and collaboration networks, and keyword clusters were analyzed using Bibliometrix and VOSviewer. Following the PRISMA protocol, 21 publications meeting the inclusion criteria were included in the qualitative review. The findings reveal a steady increase in publication volume, a high level of co-authorship, and a production geography focused on China and the United States. Resource distribution is concentrated in engineering and acoustics journals, with deep learning and machine learning at the center of the conceptual network. Thematically, four axes have emerged: parametric and generative acoustic design, soundscape and urban environment, AI-assisted installations, and media architecture, as well as architectural/room acoustics. The study shows that the field has transitioned from an early exploration phase to application-focused maturity, and that artificial intelligence has become a design actor transforming the spatial auditory experience. Additionally, it is recommended that future research be deepened through experimental studies along these four thematic axes and supported by broader bibliometric analyses based on multiple databases.

Kaynakça

  • Acar, A. K. (2025). Yeni medya teknolojilerinde müzisyen kimliğinin dönüşümü: Yapay zekâ, metaverse ve dijital dönüşüm kavramları üzerine bibliyometrik analiz. Online Journal of Music Sciences, 10(2), 219-234. https://doi.org/10.31811/ojomus.1624090
  • Ajiboye, S. A. (2024). Harmonizing spaces: Investigating the intersection of sound and architectural design. Studies in Art and Architecture, 3(3), 46–56.
  • Aria, M. and Cuccurullo, C. (2017). Bibliometrix: An R tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  • Barbosa, A. and Tsang, T. (2017). Sounding architecture: Inter-disciplinary studio at HKU. Proceedings of the 2017 International Conference on New Interfaces for Musical Expression (NIME), Blacksburg, VA.
  • Bevilacqua, A. and Iannace, G. (2023). Acoustic study of the Roman theatre of Pompeii: Comparison between existing condition and future installation of two parametric acoustic shells. The Journal of the Acoustical Society of America, 154(4), 2211–2226. https://doi.org/10.1121/10.0021312
  • Bisig, D., Schacher, J., and Neukom, M. (2011). Flowspace – A hybrid ecosystem. Proceedings of the International Conference on New Interfaces for Musical Expression (NIME 2011), 260-263.
  • Boes, M., Filipan, K., De Coensel, B. and Botteldooren, D. (2018). Machine listening for park soundscape quality assessment. Acta Acustica united with Acustica, 104(1), 121–130.
  • Bucaro, J. A., Waters, Z. J., Houston, B. H., Simpson, H. J., Sarkissian, A., Dey, S., and Yoder, T. J. (2012). Acoustic identification of buried underwater unexploded ordnance using a numerically trained classifier (L). The Journal of the Acoustical Society of America, 132(6), 3614–3617. https://doi.org/10.1121/1.4763997
  • Büyükkaragöz, T. (2025). Sanatta yapay zekâ ve bir öncü: Refik Anadol’un veri estetiği. International Social Sciences Studies Journal, 11(8), 1379–1394.
  • Caramiaux, B., Françoise, J., Schnell, N. and Bevilacqua, F. (2014). Mapping through listening. Computer Music Journal, 38(3), 34–48.
  • Carnovalini, F. and Rodà, A. (2020). Computational creativity and music generation systems: An introduction to the state of the art. Frontiers in Artificial Intelligence, 3, 14. https://doi.org/10.3389/frai.2020.00014
  • Cengizoglu, F. P. (2024). Bibliometric analysis on artificial intelligence aided architectural design. Journal of Design Studio, 6(2), 231-245. https://doi.org/10.46474/jds.1525949
  • Chen, Y., Huang, L. and Gou, T. (2024). Applications and Advances of Artificial Intelligence in Music Generation: A Review. ArXiv. https://arxiv.org/abs/2409.03715
  • D’Alessandro, N., Calderon, R. and Müller, S. (2011). ROOM#81 – Agent-based instrument for experiencing architectural and vocal cues. Proceedings of the International Conference on New Interfaces for Musical Expression (NIME 2011), 132–135.
  • Delikanlı, B. and Çağdaş, G. (2021). Hesaplamalı tasarımda disiplinler ötesi kavramlar ve eğitime yansımaları. Mimarlıkta Sayısal Tasarım XV. Ulusal Sempozyumu (28–29 Haziran 2021, İstanbul) (ss. 93–104). İstanbul: İTÜ Yayınevi.
  • Dziwis, D., von Coler, H. and Pörschmann, C. (2023). Orchestra: a toolbox for live music performances in a Web-Based metaverse. Journal of the Audio Engineering Society, 71(11), 802–812. https://doi.org/10.17743/jaes.2022.0096
  • Einbond, A., Bresson, J., Schwarz, D. and Carpentier, T. (2021). Instrumental radiation patterns as models for corpus-based spatial sound synthesis: Cosmologies for piano and 3D electronics. In Proceedings of the International Computer Music Conference 2021 (pp. 148–153).
  • Engel, J., Hantrakul, L., Gu, C. and Roberts, A. (2020). DDSP: Differentiable digital signal processing. arXiv. https://doi.org/10.48550/arxiv.2001.04643
  • Glass, A. (2020). An algorithmic design method and a three-dimensional active simulation program for acoustically functional halls. (Unpublished doctoral dissertation). Mimar Sinan Fine Arts University, İstanbul, Türkiye.
  • Knees, P., Schedl, M. and Goto, M. (2020). Intelligent user interfaces for music discovery. Transactions of the International Society for Music Information Retrieval, 3(1), 165–179. https://doi.org/10.5334/tismir.60
  • Liu, D., Shahid, Z., Tung, Y., Muliana, A., Ham, Y., Kalantar, N., Chaspari, T., Green, E. and Hubbard, J. E. (2023). Tunable acoustic properties in reconfigurable kerf structures. Journal of Architectural Engineering, 29(3). https://doi.org/10.1061/jaeied.aeeng-1539
  • Lluís, F., Martínez-Nuevo, P., Møller, M. B. and Shepstone, S. E. (2020). Sound field reconstruction in rooms: Inpainting meets super-resolution. The Journal of the Acoustical Society of America, 148(2), 649–659.
  • Oldoni, D., De Coensel, B., Boes, M., Rademaker, M., De Baets, B. and Botteldooren, D. (2013). A computational model of auditory attention for use in soundscape research. The Journal of the Acoustical Society of America, 134(1), 852–861.
  • Özdemir, M. and Arslan-Selçuk, S. (2021). Mimarlıkta makine öğrenmesi: Bibliyometrik bir analiz. Online Journal of Art and Design, 9(4), 194-207.
  • Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
  • Pošćić, A. and Kreković, G. (2020b). On the Human Role in Generative Art: A Case Study of AI-driven Live Coding. Journal of Science and Technology of the Arts, 12(3), 45–62. https://doi.org/10.34632/jsta.2020.9488
  • Schedl, M. and Flexer, A. (2012). Putting the User in the Center of Music Information Retrieval. International Society for Music Information Retrieval Conference.
  • Stefani, D. and Turchet, L. (2023). Real-Time Embedded Deep Learning on Elk Audio OS. IEEE, 1–10. https://doi.org/10.1109/ieeeconf59510.2023.10335204
  • Taşcı, M. H. and Öztürk, B. (2021). From sound and volume to music and space: The relationship of music and concert halls at the Istanbul Music Festival, Hagia Irene Museum and Lütfi Kırdar Hall in 3rd International Hagia Sophia Multidisciplinary Scientific Research Congress.
  • Thorogood, M., Fan, J. and Pasquier, P. (2019). A framework for computer-assisted sound design systems supported by modelling affective and perceptual properties of soundscape. Journal of New Music Research, 48(3), 264–280.
  • Van Eck, N. J. and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
  • Wang, F., Chen, Z. and Wu, C. (2019). Applicability of artificial neural network to estimate sound transmission loss of ultrafine glass fiber felts. Acta Acustica United With Acustica, 105(4), 650–656. https://doi.org/10.3813/aaa.919345
  • Wang, L. and Cavallaro, A. (2022). Deep-learning-assisted sound source localization from a flying drone. IEEE Sensors Journal, 22(21), 20828-20838.
  • Whalley, I. (2015). Developing Telematic Electroacoustic Music: Complex networks, machine intelligence and affective data stream sonification. Organised Sound, 20(1), 90–98. doi:10.1017/S1355771814000478
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Müzikoloji ve Etnomüzikoloji
Bölüm Araştırma Makalesi
Yazarlar

Fazıl Akdağ 0000-0002-3316-8104

Fatma Betül Künyeli 0000-0002-6189-5966

Gönderilme Tarihi 2 Kasım 2025
Kabul Tarihi 4 Aralık 2025
Erken Görünüm Tarihi 10 Aralık 2025
Yayımlanma Tarihi 31 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 4

Kaynak Göster

APA Akdağ, F., & Künyeli, F. B. (2025). FROM ALGORITHMS TO ACOUSTICS: A SCIENTIFIC MAPPING OF AI-BASED APPROACHES IN MUSIC AND ARCHITECTURAL DESIGN INTERSECTION. Yegah Müzikoloji Dergisi, 8(4), 4105-4139. https://doi.org/10.51576/ymd.1815785


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