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INTELLIGENT MUSIC APPLICATIONS: INNOVATIVE SOLUTIONS FOR MUSICIANS AND LISTENERS

Yıl 2023, Cilt: 7 Sayı: 3, 752 - 773, 30.09.2023
https://doi.org/10.47525/ulasbid.1324070

Öz

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.

Kaynakça

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AKILLI MÜZİK UYGULAMALARI: MÜZİSYENLER VE DİNLEYİCİLER İÇİN YENİLİKÇİ ÇÖZÜMLER

Yıl 2023, Cilt: 7 Sayı: 3, 752 - 773, 30.09.2023
https://doi.org/10.47525/ulasbid.1324070

Öz

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.

Kaynakça

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  • 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
Toplam 88 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Müzik (Diğer)
Bölüm Makaleler
Yazarlar

Cihan Tabak 0000-0002-1060-9499

Erken Görünüm Tarihi 20 Eylül 2023
Yayımlanma Tarihi 30 Eylül 2023
Gönderilme Tarihi 7 Temmuz 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 7 Sayı: 3

Kaynak Göster

APA Tabak, C. (2023). INTELLIGENT MUSIC APPLICATIONS: INNOVATIVE SOLUTIONS FOR MUSICIANS AND LISTENERS. Uluslararası Anadolu Sosyal Bilimler Dergisi, 7(3), 752-773. https://doi.org/10.47525/ulasbid.1324070

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