AKILLI MÜZİK UYGULAMALARI: MÜZİSYENLER VE DİNLEYİCİLER İÇİN YENİLİKÇİ ÇÖZÜMLER

Yapay zekâ ve makine öğreniminin akıllı müzik uygulamalarına entegrasyonu, müzikal ifade için yeni yollar da açmaktadır. Bu uygulamalar, müzikte aktarılan duyguları analiz edip yorumlayarak duygusal olarak duyarlı bestelerin oluşturulmasını sağlarken diğer yandan farklı konumlardaki müzisyenleri birbirine bağlayarak ve bulut tabanlı platformlar aracılığıyla gerçek zamanlı iş birliğine olanak sağlayarak iş birliğine dayalı müzik yapımını kolaylaştırmaktadır. Bu araştırmanın amacı, teknolojiyle yakın bir ilişkisi olan müziğin; üretim, dağıtım ve tüketim kalıpları hakkında bilgi vermektir. Doküman analizi yöntemi kullanılan araştırmada yapay zekâ ve makine öğrenimini müzik endüstrisine entegre etmenin potansiyel faydaları, farklı bakış açılarıyla ileriye dönük modeller ve kullanım alanları incelenmiştir. Ayrıca gelecekteki araştırmaların, yapay zekâ ve makine öğrenimi algoritmalarını iyileştirmeye, bunların sorumlu ve etik bir şekilde uygulanmasını sağlama ve müzikal yenilik için yeni olasılıklar hakkında görüşler belirtilmiştir.

Anahtar Kelimeler:

akıllı müzik, yapay zeka, müzik üretimi

INTELLIGENT MUSIC APPLICATIONS: INNOVATIVE SOLUTIONS FOR MUSICIANS AND LISTENERS

The incorporation of artificial intelligence and machine learning into intelligent music applications presents fresh avenues for musical expression. These applications allow the production of emotionally responsive pieces by analysing and interpreting the emotions conveyed within music. Furthermore, they aid collaborative music-making by connecting musicians in diverse locations and enabling real-time collaboration via cloud-based platforms. The objective of this research is to present information regarding the production, distribution, and consumption of music, which has a close association with technology. Through document analysis, the prospective advantages of incorporating artificial intelligence and machine learning into the music industry are assessed from diverse vantage points, analysing potential models and areas of application. It also proposes further research to enhance artificial intelligence and machine learning algorithms, guaranteeing their responsible and ethical use, and unlocking new avenues for musical innovation.

Keywords:

Intelligent music, artificial intelligence, music production.,

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Ábel, K., Somlai, F., & Szabo, A. (2023). Acute mental benefits of aquatic exercises in middle-aged women, Mentálhigiéné és Pszichoszomatika, 24(1), 75-81. https://doi.org/10.1556/0406.2023.00007
Alaraj, A., Lemole, M. G., Finkle, J. H., Yudkowsky, R., Wallace, A., Luciano, C., Banerjee, P. P., Rizzi, S. H., & Charbel, F. T. (2011). Virtual reality training in neurosurgery: Review of current status and future applications. Surgical Neurology International, 2, 52. https://doi.org/10.4103/2152-7806.80117
Andono, P. N., Noersasongko, E., Shidik, G. F., Hastuti, K., Sudaryanto, S., & Syarif, A. M. (2022). Melody difficulty classification using frequent pattern and inter-notes distance analysis. International Journal of Advanced Computer Science and Applications, 13(2). https://doi.org/10.14569/ijacsa.2022.0130215
Armando, N., Almeida, R., Fernandes, J., Silva, J., & Boavida, F. (2021). End-to-end experimentation of a 5G vertical within the scope of blended learning. Discover Internet of Things, 1(1). https://doi.org/10.1007/s43926-021-00008-5
Bayley, J., & Waldron, J. (2020). “It’s never too late”: Adult Students and music learning in one online and offline convergent community music school. International Journal of Music Education, 38(1), 36-51. https://doi.org/10.1177/0255761419861441
Benedict, C., & O’Leary, J. (2019). Reconceptualizing “music making:” music technology and freedom ın the age of neoliberalism. Action, Criticism, and Theory for Music Education, 18(1), 26-43. https://doi.org/10.22176/act18.1.26
Beukelaer, C., & Eisenberg, A. J. (2018). Mobilising African music: How mobile telecommunications and technology firms are transforming African music sectors. Journal of African Cultural Studies, 32(2), 195-211. https://doi.org/10.1080/13696815.2018.1546569
Biasutti, M., Philippe, R., & Schiavio, A. (2021). Assessing teachers’ perspectives on giving music lessons remotely during the covid-19 lockdown period. Musicae Scientiae, 26(3), 585-603. https://doi.org/10.1177/1029864921996033
Bidelman, G., Hutka, S., & Moreno, S. (2013). Tone language speakers and musicians share enhanced perceptual and cognitive abilities for musical pitch: evidence for bidirectionality between the domains of language and music. PLoS ONE, 8(4), e60676. https://doi.org/10.1371/journal.pone.0060676
Boehm, C., Hepworth-Sawyer, R., Hughes, N., & Ziemba, D. (2018). The discipline that ‘became’: Developments in music technology in British higher education between 2007 and 2018. Journal of Music Technology and Education, 11(3), 251-267. https://doi.org/10.1386/jmte.11.3.251_1
Buonviri, N., & Paney, A. (2020). Technology use in high school aural skills instruction. International Journal of Music Education, 38(3), 431-440. https://doi.org/10.1177/0255761420909917
Cádiz, R. (2020). Creating music with fuzzy Logic. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00059
Cao, H. (2021). Innovation and practice of music education paths in universities under the popularity of 5G network. Wireless Communications and Mobile Computing, 2021, 1-11. https://doi.org/10.1155/2021/3570412
Cianconi, P., Betrò, S., & Janiri, L. (2020). The impact of climate change on mental health: a systematic descriptive review. Frontiers in Psychiatry, (11). https://doi.org/10.3389/fpsyt.2020.00074
Chambers, P. (2021). Producing the self: Digitisation, music-making and subjectivity. Journal of Sociology, 58(4), 554-569. https://doi.org/10.1177/14407833211009317
Chen, F., & Diao, C. (2023). Research on music visualization in new media. BCP Education & Psychology, 8, 71-77. https://doi.org/10.54691/bcpep.v8i.4274
Cho, S., Baek, Y., & Choe, E. (2018). A strategic approach to music listening with a mobile app for high school students. International Journal of Music Education, 37(1), 132-141. https://doi.org/10.1177/0255761418819016
Criscuolo, A., Bonetti, L., Särkämö, T., Kliuchko, M., & Brattico, E. (2019). On the association between musical training, intelligence and executive functions in adulthood. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.01704
Cobos, M., Ahrens, J., Kowalczyk, K., & Politis, A. (2022). An overview of machine learning and other data-based methods for spatial audio capture, processing, and reproduction. Journal on Audio, Speech, and Music Processing, 10. https://doi.org/10.1186/s13636-022-00242-x
Concina, E. (2019). The role of metacognitive skills in music learning and performing: Theoretical features and educational implications. Frontiers in Psychology, (10). https://doi.org/10.3389/fpsyg.2019.01583
Corbin, J. & Strauss, A. (2008). Basics of qualitative research: Techniques and procedures for developing grounded theory (3rded.). Sage Publications, Inc.
Dang, A., Arora, D., & Rane, P. (2020). Role of digital therapeutics and the changing future of healthcare. Journal of Family Medicine and Primary Care, 9(5), 2207–2213. https://doi.org/10.4103/jfmpc.jfmpc_105_20
Deruty, E., Grachten, M., Lattner, S., Nistal, J., & Aouameur, C. (2022). On the development and practice of ai technology for contemporary popular music production. Transactions of the International Society for Music Information Retrieval, 5(1), 35-49. https://doi.org/10.5334/tismir.100
Frytsiuk, V. A., Brylin, B. A., Zanalnyuk, A. F., Frytsiuk, V. M., & Mykhaylyshen, A. V. (2022). Implementation of information technology into the education of music teachers. Journal of Higher Education Theory and Practice, 22(6), 35-43. https://doi.org/10.33423/jhetp.v22i6.5226
Gallidabino, M. D., Barron, L. P., Weyermann, C., & Romolo, F. S. (2019). Quantitative profile–profile relationship (QPPR) modelling: a novel machine learning approach to predict and associate chemical characteristics of unspent ammunition from gunshot residue (GSR). Analyst, 144, 1128–1139. https://doi.org/10.1039/C8AN01841C
Gong, T., & Han, C. (2022). Construction of an intelligent arrangement model for vocal music based on HPC cluster programming. Scientific Programming, 2022, 1-13. https://doi.org/10.1155/2022/9728085
Gorgoglione, M., Garavelli, A., Panniello, U., & Natalicchio, A. (2023). Information retrieval technologies and big data analytics to analyze product innovation in the music industry. Sustainability, 15(1), 828. https://doi.org/10.3390/su15010828
Gu, Z. (2023). Analysis of the relationship between physical exercise and mental health. LNEP, 6(1), 534-538. https://doi.org/10.54254/2753-7048/6/20220472
Herbst, J., Reuter, C., & Czedik-Eysenberg, I. (2018). Guitar profiling technology in metal music production: Public reception, capability, consequences and perspectives. Metal Music Studies, 4(3), 481-506. https://doi.org/10.1386/mms.4.3.481_1
Hides, L., Dingle, G., Quinn, C., Stoyanov, S., Zelenko, O., Tjondronegoro, D., … & Kavanagh, D. (2019). Efficacy and outcomes of a music-based emotion regulation mobile app in distressed young people: Randomized controlled trial. JMIR mHealth uHealth, 7(1), e11482. https://doi.org/10.2196/11482
Honzel, E., Murthi, S., Brawn-Cinani, B., Colloca, G., Kier, C., Varshney, A., & Colloca, L. (2019). Virtual reality, music, and pain: developing the premise for an interdisciplinary approach to pain management. Pain, 160(9), 1909–1919. https://doi.org/10.1097/j.pain.0000000000001539
Huang, C. (2020). An innovative method of algorithmic composition using musical tension. Multimedia Tools and Applications, 79, 32119-32136. https://doi.org/10.1007/s11042-020-09506-0
Hwang, A., & Lee, J. (2023). Studying of UX design of music streaming application with big data analysis for seniors. Asia-Pacific Journal of Convergent Research Interchange, 9(1), 205-214. https://doi.org/10.47116/apjcri.2023.01.17
Hwang, W. J., Ha, J. S., & Kim, M. J. (2021). Research trends on mobile mental health application for general population: A scoping review. International Journal of Environmental Research and Public Health, 18(5), 2459. https://doi.org/10.3390/ijerph18052459
Jiang, Q. (2022). Application of artificial intelligence technology in music education supported by wireless network. Mathematical Problems in Engineering, 1-11. https://doi.org/10.1155/2022/2138059
Jin, C., Wu, F., Wang, J., Liu, Y., Guan, Z., & Han, Z. (2022). MetaMGC: A music generation framework for concerts in Metaverse. EURASIP Journal on Audio, Speech, and Music Processing, 2022. https://doi.org/10.1186/s13636-022-00261-8
Jossa-Bastidas, O., Sanchez, A., Bravo-Lamas, L., & Garcia-Zapirain, B. (2023). IoT system for gluten prediction in flour samples using nirs technology, Deep and Machine Learning Techniques. Electronics, 12(8), 1916. https://doi.org/10.3390/electronics12081916
Koder, J., Dun, J., & Rhodes, P. (2023). Climate distress: a review of current psychological research and practice. Sustainability, 15(10), 8115. https://doi.org/10.3390/su15108115
Kulinski, J., Ofori, E. K., Visotcky, A., Smith, A., Sparapani, R., & Fleg, J. L. (2022). Effects of music on the cardiovascular system. Trends in Cardiovascular Medicine, 32(6), 390–398. https://doi.org/10.1016/j.tcm.2021.06.004 Längler, M., Brouwer, J., Timmermans, A., & Gruber, H. (2021). Exploring change in networks supporting the deliberate practice of popular musicians. Psychology of Music, 50(2), 439-459. https://doi.org/10.1177/03057356211003961
Leonard, J., Villeneuve, J., & Kontogeorgakopoulos, A. (2020). Multisensory instrumental dynamics as an emergent paradigm for digital musical creation. Journal on Multimodal User Interfaces, 14, 235-253. https://doi.org/10.1007/s12193-020-00334-y
Li, H. (2021). Piano education of children using musical instrument recognition and deep learning technologies under the educational psychology. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.705116
Li N. (2022). Combination of blockchain and ai for music intellectual property protection. Computational Intelligence and Neuroscience, 2022, 4482217. https://doi.org/10.1155/2022/4482217
Li, S., Jang, S., & Sung, Y. (2019). Automatic melody composition using enhanced GAN. Mathematics, 7(10). https://doi.org/10.3390/math7100883
Liu, C., Hwang, G.-J., Tu, Y.-f., Yin, Y., & Wang, Y. (2021). Research advancement and foci of mobile technology-supported music education: A systematic review and social network analysis on 2008-2019 academic publications. Interactive Learning Environments. Advance Online Publication. https://doi.org/10.1080/10494820.2021.1974890
Lei, S., Chiu, D., Lung, M., & Chan, C. (2021). Exploring the aids of social media for musical instrument education. International Journal of Music Education, 39(2), 187-201. https://doi.org/10.1177/0255761420986217
Luo, J., Yang, X., Ji, S., & Li, J. (2019). MG-VAE: Deep Chinese folk songs generation with specific regional styles. In: Li, H., Li, S., Ma, L., Fang, C. & Zhu, Y. (Eds.), Proceedings of the 7th Conference on Sound and Music Technology (CSMT). 93-106. Springer. https://doi.org/10.1007/978-981-15-2756-2_8
Maidaniyk, I., Strikhar, O., Rudyy, R., Shelepnytska-Govorun, N., Bilova, N., & Yeroshenko, O. (2023). development of music education in postmodern society. Revista Romaneasca Pentru Educatie Multidimensionala, 15(2), 284-297. https://doi.org/10.18662/rrem/15.2/734
Mariani, M. M., Perez-Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755– 776. https://doi.org/10.1002/mar.21619
Meissner, H. (2021). Theoretical framework for facilitating young musicians’ learning of expressive performance. Frontiers in Psychology, (11). https://doi.org/10.3389/fpsyg.2020.584171
Merrick, B., & Joseph, D. (2022). ICT and music technology during Covid-19: Australian music educator perspectives. Research Studies in Music Education, 45(1), 189-210. https://doi.org/10.1177/1321103x221092927
Missingham, R. (2023). Archives, access and artificial intelligence: Working with born-digital and digitized archival collections. Journal of the Australian Library and Information Association, 72(1), 100-115. https://doi.org/10.1080/24750158.2023.2168151
Modeme, E. (2023). Using technology to enhance instruction and learning in musical arts education in Nigeria. International Journal of Current Research in the Humanities, 26(1), 124-144. https://doi.org/10.4314/ijcrh.v26i1.9
Müller, M., Arzt, A., Balke, S., Dorfer, M., & Widmer, G. (2019). Cross-modal music retrieval and applications: An overview of key methodologies. IEEE Signal Processing Magazine, 36(1), 52-62. https://doi.org/10.1109/msp.2018.2868887
Ng, D., Ng, E., & Chu, S. (2021). Engaging students in creative music making with musical instrument application in an online flipped classroom. Education and Information Technologies, 27, 45-64. https://doi.org/10.1007/s10639-021-10568-2
Nicolaou, C., Matsiola, M., Karypidou, C., Podara, A., Kotsakis, R., & Kalliris, G. (2021). Media studies, audiovisual media communications, and generations: The case of budding journalists in radio courses in Greece. Journalism and Media, 2(2), 155-192. https://doi.org/10.3390/journalmedia2020010
Norman, T. (2020). Using the iPad as a compositional and pedagogical tool. Journal of General Music Today, 34(3), 4-12. https://doi.org/10.1177/1048371320972166
Ogunbode, C., Pallesen, S., Böhm, G., Doran, R., Bhullar, N., Aquino, S., … & Lomas, M. (2021). Negative emotions about climate change are related to insomnia symptoms and mental health: Cross-sectional evidence from 25 countries. Current Psychology, 42, 845-854. https://doi.org/10.1007/s12144-021-01385-4
Özer, Z., & Demirbatır, R. (2023). Examination of STEAM-based digital learning applications in music education. European Journal of STEM Education, 8(1), 02. https://doi.org/10.20897/ejsteme/12959
Paché, G. (2023). Managing rock/pop tours: An exploration of logistical dimensions. Journal of Applied Business and Economics, 25(1). https://doi.org/10.33423/jabe.v25i1.5919
Pandeya, Y. R., Bhattarai, B., & Lee, J. (2022). Tracking the rhythm: Pansori rhythm segmentation and classification methods and datasets. Applied Sciences, 12(19), 9571. https://doi.org/10.3390/app12199571
Peretz, G., Taylor, B., Ruzek, J., Jefroykin, S., & Sadeh-Sharvit, S. (2023). Machine learning model to predict assignment of therapy homework in behavioral treatments: Algorithm development and validation. JMIR Formative Research, 7, e45156. https://doi.org/10.2196/45156
Powell, B. (2021). Modern band: A review of literature. Update: Applications of Research in Music Education, 39(3), 39-46. https://doi.org/10.1177/8755123320988528
Qureshi, N., Bhandari, S., Lorenzo, G., & Sampath, H. (2023). Editorial: Mental health promotion and protection. Frontiers in Psychiatry, (14). https://doi.org/10.3389/fpsyt.2023.1161358
Raponi, S., Oligeri, G. & Ali, I.M. (2022). Sound of guns: Digital forensics of gun audio samples meets artificial intelligence. Multimedia Tools and Applications, 81, 30387–30412. https://doi.org/10.1007/s11042-022-12612-w Reizábal, M., & Gómez, M. (2022). Learning analytics and higher music education: Perspectives and challenges. Artseduca, 34, 219-228. https://doi.org/10.6035/artseduca.6831
Revenko, V. (2021). Education and music culture in the context of web 2.0. International Journal of Emerging Technologies in Learning, 16(10), 96. https://doi.org/10.3991/ijet.v16i10.19693
Reyes, M., Carmen, B., Luminarias, M., Mangulabnan, S., & Ogunbode, C. (2021). An investigation into the relationship between climate change anxiety and mental health among gen z filipinos. Current Psychology, 42(9), 7448-7456. https://doi.org/10.1007/s12144-021-02099-3
Shakirova, E. (2017) Collaborative filtering for music recommender system. IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 548-550. https://doi.org/10.1109/EIConRus.2017.7910613
Shang, J., & Shao, F. (2022). Design of the music intelligent management system based on a deep CNN. Security and Communication Networks, 1-9. https://doi.org/10.1155/2022/1559726
Siphocly, N. N. J., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2021). Top 10 artificial intelligence algorithms in computer music composition. International Journal of Computing and Digital Systems, 10, 373–394. https://doi.org/10.12785/IJCDS/100138
Sturm, B., Iglesias, M., Ben-Tal, O., Miron, M., & Gómez, E. (2019). artificial intelligence and music: Open questions of copyright law and engineering praxis. Arts, 8(3), 115. https://doi.org/10.3390/arts8030115
Swarbrick, D., Seibt, B., Grinspun, N., & Vuoskoski, J. (2021). Corona concerts: The effect of virtual concert characteristics on social connection and Kama Muta. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.648448
Thoma, M. V., La Marca, R., Brönnimann, R., Finkel, L., Ehlert, U., & Nater, U. M. (2013). The effect of music on the human stress response. PloS ONE, 8(8), e70156. https://doi.org/10.1371/journal.pone.0070156
Thompson, Z., Tamplin, J., Sousa, T., Carrasco, R., Flynn, L., Lamb, K., … & Baker, F. (2023). Content development and validation for a mobile application designed to train family caregivers in the use of music to support care of people living with dementia. Frontiers in Medicine, 10. https://doi.org/10.3389/fmed.2023.1185818 914-928. https://doi.org/10.1108/lht-08-2021-0248
Turchet, L., Lagrange, M., Rottondi, C., Fazekas, G., Peters, N., Østergaard, J., … & Fischione, C. (2023). The internet of sounds: convergent trends, insights, and future directions. IEEE Internet of Things Journal, 10(13), 11264-11292. https://doi.org/10.1109/jiot.2023.3253602
Tuuri, K., & Koskela, O. (2020). Understanding human–technology relations within technologization and appification of musicality. Frontiers in Psychology, (11). https://doi.org/10.3389/fpsyg.2020.00416
Verma, S. (2021). Artificial intelligence and music: History and the future perceptive. International Journal of Applied Research, 7(2), 272-275. https://doi.org/10.22271/allresearch.2021.v7.i2e.8286
Wang, Y. (2023). Can gamification assist learning? A study to design and explore the uses of educational music games for adults and young learners. Journal of Educational Computing Research, 60(8), 2015-2035. https://doi.org/10.1177/07356331221098148
Wang, S., & Yu, W. (2020). Space elements of computer music production based on VR technology. IEEE Access, 1-1. https://doi.org/10.1109/access.2020.3019457
Weng, S., & Chen, H. (2020). Exploring the role of deep learning technology in the sustainable development of the music production industry. Sustainability, 12(2), 625. https://doi.org/10.3390/su12020625
Wilson, R. (2020). Aesthetic and technical strategies for networked music performance. AI & SOCIETY. https://doi.org/10.1007/s00146-020-01099-4
Xiang, Y. (2022). Analysis of psychological shaping function of music education under the background of artificial intelligence. Journal of Environmental and Public Health, 1-14. https://doi.org/10.1155/2022/7162069 Xu, K. (2020). Establishment of music emotion model based on blockchain network environment. Wireless Communications and Mobile Computing, 1-7. https://doi.org/10.1155/2020/8870886
Yang, T. & Nazir, S. (2022). A comprehensive overview of ai-enabled music classification and its influence in games. Soft Computing, 26, 7679-7693. https://doi.org/10.1007/s00500-022-06734-4
Yao, B., & Weiwei, L. (2023). The role of a teacher in modern music education: Can a student learn music with the help of modernized online educational technologies without teachers?. Education and Information Technologies. https://doi.org/10.1007/s10639-023-11786-6
Yu, X., Ma, N., Zheng, L., Wang, L., & Wang, K. (2023). Developments and applications of artificial intelligence in music education. Technologies, 11(2), 42. https://doi.org/10.3390/technologies11020042
Yoo, H. (2022). Building 21st century skills through technology in general music classes. Journal of General Music Education, 36(1), 21-31. https://doi.org/10.1177/27527646221110867
Zhao, Y. (2022). Analysis of music teaching in basic education integrating scientific computing visualization and computer music technology. Mathematical Problems in Engineering, 1-12. https://doi.org/10.1155/2022/3928889
Zhao, X., Tuo, Q., Guo, R., & Kong, T. (2022). Research on music signal processing based on a blind source separation algorithm. Annals of Emerging Technologies in Computing, 6(4), 24-30. https://doi.org/10.33166/aetic.2022.04.003
Zhen-Wu, N. (2022). Application research of bel canto performance based on artificial intelligence technology. Applied Mathematics and Nonlinear Sciences, https://doi.org/10.2478/amns.2021.2.00255