Artificial Intelligence and the Integration of the Industrial Revolution 6.0 in Ethnomusicology: Demands, Interventions and Implications

Year 2024, Volume: 8 Issue: 1, 75 - 107, 30.06.2024

Glinore S. Morales , Mary Leigh Ann C. Perez , Almighty C. Tabuena

https://doi.org/10.33906/musicologist.1286472

Abstract

This paper focuses on the integration of Artificial Intelligence (AI) and Industrial Revolution 6.0 in ethnomusicology, which is the study of music from diverse cultures. The challenges facing researchers in ethnomusicology necessitate the use of AI, which can aid in analyzing, transcribing, and preserving musical traditions. Furthermore, AI can be utilized to generate new musical compositions that integrate elements from various musical traditions. Meanwhile, the Industrial Revolution 6.0 technologies, such as AI, can facilitate the preservation and distribution of musical traditions, cross-cultural understanding, and new forms of music education through virtual reality and immersive experiences. However, the integration of these technologies also raises concerns about cultural exploitation and the authenticity of musical traditions. This study aims to examine the demands, interventions, and implications of AI and Industrial Revolution 6.0 in ethnomusicology, emphasizing the need for a collaborative and culturally sensitive approach that addresses ethical considerations, handles bias and accuracy, and balances AI with traditional methods of music analysis and interpretation. Ultimately, the study suggests that practitioners and researchers must approach the demands of AI and Industrial Revolution 6.0 with caution, consideration, and a spirit of collaboration in order to realize their potential advantages for ethnomusicology while avoiding ethical and cultural pitfalls.

Keywords

Artificial Intelligence, Demands, Ethnomusicology, Implications, Industrial Revolution 6.0, Interventions

References

• Ajibade, Olufunke Olupero; Leon-Gomez, Rodolf; Tabuena, Almighty Cortezo; Alase, Peter O.; Huaman, Jovencio Ticsihua; Adediran, Anthonia Oluwatosin and Ayaz, Muhammad. (2023, June). “New Insights into the Research of Social Media Marketing and Consumer Behaviour: A Scientometric Analysis of a Decade”. In 2023 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) (pp. 143-148). IEEE.
• Ajibade, Samuel-Soma M.; Mejarito, Cresencio; Chin, Dindo M.; Dayupay, Johnry P.; Gido, Nathaniel G.; Tabuena, Almighty C;.; Chaudhury, Sushovan and Bassey, Mbiatke Anthony. (2023, March). “Teacher’s attitudes towards improving inter-professional education and innovative technology at a higher institution: A cross-sectional analysis” Innovations in Bio-Inspired Computing and Applications: Proceedings of the 13th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2022) Held During December 15-17, 2022 (pp. 713-724). Cham: Springer Nature Switzerland.
• Ajibade, Samuel-Soma M.; Oyebode, Oluwadare Joshua; Dayupay, Johnry P.; Gido, Nathaniel G.; Tabuena, Almighty C. and Kilag, Osias Kit T. (2022). “Data classification technique for assessing drug use in adolescents in secondary education” Journal of Pharmaceutical Negative Results, 13(4), 971-977.
• Al Momani, Kamelia Moh’d Khier; Nour, Abdul-Naser Ibrahim.; Jamaludin, Nurasyikin and Zanani Wan Abdullah, Wan Zalani Wan. (2021). “Fourth industrial revolution, artificial intelligence, intellectual capital, and covid-19 pandemic”. In: Hamdan, A., Hassanien, A.E., Khamis, R., Alareeni, B., Razzaque, A., Awwad, B. (eds) Applications of Artificial Intelligence in Business, Education and Healthcare, Studies in Computational Intelligence, vol 954. Springer, Cham. https://doi.org/10.1007/978-3-030-72080-3_5
• American University. (2019, December 16). “Virtual Reality in Education: Benefits, Tools, and Resources” School of Education, American University. https://soeonline.american.edu/blog/benefits-of-virtual-reality-in-education/
• Amilevičius, Darius. (2020). “Machine Bias and Fundamental Rights” Smart Technologies and Fundamental Rights, Ed. John-Stewart Gordon: (pp. 334-365). Brill.
• Balato, Merrenisa E.; Perez, Mary Leigh Ann C.; Morales, Glinore S. and Tabuena, Almighty C. (2023). “Advantages children may gain from participating in creative dance lessons while attending a public elementary school” Journal of Humanities, Music and Dance, 3(1), 1-12.
• Bartels, Gerald; Bencherki, Nicolas. (2020). “Actor-network-theory and creativity research”. Encyclopedia of creativity, invention, innovation and entrepreneurship, Ed. Elias G. Carayannis: pp. 29-36.
• Bates, Eliot. (2016). Digital Tradition: Arrangement and Labor in Istanbul's Recording Studio Culture. Oxford University Press.
• Benetos, Emmanouil; Dixon, Simon; Duan, Zhiyao and Ewert, Sebastian. (2018). “Automatic music transcription: An overview” IEEE Signal Processing Magazine, 36(1), 20-30.
• Blackwell, Tim; Bown, Oliver and Young, Michael. (2012). “Live algorithms: Towards autonomous computer improvisers” Computers and creativity, Ed. Jon McCormack, Mark d’Inverno: pp. 147-174. Springer.
• Born, Georgina. (2020). “Diversifying MIR: Knowledge and real-world challenges, and new interdisciplinary futures” Transactions of the International Society for Music Information Retrieval, 3(1).
• Bracknell, Clint. (2015). “‘Say You’re a Nyungarmusicologist’: Indigenous Research and Endangered Song” Musicology Australia, 37(2), 199-217.
• Bravo, Carrie Danae S.; Dimalanta, Feliz Danielle R.; Jusay, Kate Ashley P.; Vitug, Martina Ysabel and Tabuena, Almighty C. (2022). “Inclination state on the Philippine culture and arts using the appraisal theory: Factors of progress and deterioration” Participatory Educational Research, 9(1): 388-403.
• Brusila, Johannes; Cloonan, Martin and Ramstedt, Kim. (2022). “Music, digitalization, and democracy” Popular music and society, 45(1): 1-12.
• Casey, Michael A.; Veltkamp, Remco; Goto, Masataka; Leman, Marc; Rhodes, Christophe and Slaney, Malcolm. (2008). “Content-based music information retrieval: Current directions and future challenges”. Proceedings of the IEEE, 96(4): 668-696.
• Buenaflor, Mhelmafa P.; Tabuena, Almighty C.; Morales, Glinore S. and Perez, Mary Leigh Ann C. (2022). “Associated determinants and music genres in a few fitness facilities” Journal of Humanities, Music and Dance, 2(6): 16-24.
• Cassar, Claudine. (2023). “The Role of Music in Different Cultures: A Look at Ethnomusicology” Anthropology Review. Retrieved from https://anthropologyreview.org/anthropology-glossary-of-terms/ethnomusicology-the-study-of-music-across-cultures/
• Chudy, Magdalena; Łukasik, Ewa; Parkoła, Tomasz; Kuśmierek, Ewa; Jackowski, Jacek and Dahlig-Turek, Ewa. (2020, August). “Digital Library Adaptation for Traditional Music and Content-Based Research: Polish Sound Archives and dLibra” Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 (pp. 289-298).
• Cuncic, Arlin. (2022). “What Is Cultural Appropriation?” Verywell Mind. https://www.verywellmind.com/what-is-cultural-appropriation-5070458
• Dushniy, Аndriy; Salii, Volodymyr; Storonska, Nataliia; Ulych, Hanna and Zaets, Vіtalіy. (2022). “Performing musicology in the socio-cultural space of Ukraine of the XXI century: scientific and creative discourse” Amazonia Investiga, 11(54): 137-145.
• Dwivedi, Yogesh K.; Hughes, Laurie; Ismagilova, Elvira; Aarts, Gert; Coombs, Crispin; Crick, Tom; Duan, Yanqing; Dwivedi, Rohita; Edwards, John; Eirug, Aled; Galanos, Vassilis; Ilavarasan, Vigneswara P.; Janssen, Marijn; Jones, Paul; Kar, Arpan Kumar; Kizgin, Hatice; Kronemann, Bianca; Lal, Banita; Lucini, Biagio; Medaglia, Rony and Williams, Michael D. (2021). “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy” International Journal of Information Management, 57, 101994. Falk, Catherine; Ingram, Catherine. (2011). “From intangible cultural heritage to collectable artefact: the theory and practice of enacting ethical responsibilities in ethnomusicological research” Transmission of academic values in Asian Studies workshop (2009: Australian National University, Canberra, ACT). The Australia-Netherlands Research Collaboration (ANRC).
• Furste, Zachary Aaron. (2017). “Finding Media: Recordings from Elsewhere, 1936–1965” PhD Dissertation, Harvard University, Cambridge: USA.
• Franke, Jonas. (2019). Requirements and Use Cases for Digital Sound Archives in Ethnomusicology. Computational Phonogram Archiving, Ed. Rolf Bader: pp. 229-248. Springer.
• Gómez-Cañón, Juan Sebastián; Cano, Estefanía; Eerola, Tuomas; Herrera, Perfecto; Hu, Xiao; Yang, Yi-Hsuan; and Gómez, Emilia. (2021). “Music emotion recognition: Toward new, robust standards in personalized and context-sensitive applications” IEEE Signal Processing Magazine, 38(6), 106-114.
• Grant, Catherine. (2018). “Academic flying, climate change, and ethnomusicology: personal reflections on a professional problem” Ethnomusicology Forum (Vol. 27, No. 2, pp. 123-135). Routledge.
• Gubner, Jennie. (2018). “The music and memory project: understanding music and dementia through applied ethnomusicology and experiential filmmaking” Yearbook for Traditional Music, 50, 15-40.
• Hacker, Janine; Vom Brocke, Jan; Handali, Joshua; Otto, Markus; and Schneider, Johannes. (2020). “Virtually in this together–how web-conferencing systems enabled a new virtual togetherness during the COVID-19 crisis” European Journal of Information Systems, 29(5), 563-584.
• Harrison, Klisala. (2016). “Why Applied Ethnomusicology?” Applied Ethnomusicology in Institutional Policy and Practice, Ed. Klisala Harrison: pp.1-21. COLLeGIUM: Studies across disciplines in the humanities and social sciences, Finland: University of Helsinki.
• Hartley, Paul. (2022). Radical Human Centricity: Fulfilling the Promises of Innovation Research. Anthem Press.
• Hesmondhalgh, David. (2008). “Towards a critical understanding of music, emotion and self‐identity” Consumption, markets and culture, 11(4), 329-343.
• Holzapfel, Andre; Sturm, Bob; and Coeckelbergh, Mark. (2018). “Ethical dimensions of music information retrieval technology” Transactions of the International Society for Music Information Retrieval, 1(1): 44-55.
• Hong Yun, Zou; Alshehri, Yasser; Alnazzawi, Noha; Ullah, Ijaz; Noor, Salma; and Gohar, Neelam. (2022). “A decision-support system for assessing the function of machine learning and artificial intelligence in music education for network games” Soft Computing, 26(20): 11063-11075.
• Istvandity, Lauren. (2021). “How does music heritage get lost? Examining cultural heritage loss in community and authorised music archives” International Journal of Heritage Studies, 27(4): 331-343.
• Joshi, Pradeep; Singh, Praveen; Tabuena, Alchiny C.; and Galapate, Wendell L. (2023). “Role of corporate social responsibility in talent acquisition and retention: A cross-sectional study” Journal of Informatics Education and Research, 3(1): 189-194.
• Kalaiarasi, S.; Mehta, Ankit; Bordia, Devyash; and Sanskar, D. (2019). “Using global terrorism database (GTD) and machine learning algorithms to predict terrorism and threat” International Journal of Engineering and Advanced Technology, 9(1), 5995-6000.
• Kantaros, Antreas; Ganetsos, Theodore; and Petrescu, Florian Ion Tiberiu. (2023). “Three-Dimensional Printing and 3D Scanning: Emerging Technologies Exhibiting High Potential in the Field of Cultural Heritage” Applied Sciences, 13(8): 4777.
• Khulusi, Richard; Kusnick, Jakob; Meinecke, Christofer; Gillmann, Christina; Focht, Josef; and Jänicke, Stefan. (2020). “A survey on visualizations for musical data” In Computer Graphics Forum 39(6): 82-110).
• Lahiri-Roy, Reshmi; and Belford, Nish. (2021). “‘A Neo-colonial Education’: Querying its Role in Immigrant Identity, Inclusion and Empowerment” Journal of Intercultural Studies, 42(2): 235-252.
• Landau, Carolyn; Topp Fargion, Janet. (2012). “We're All Archivists Now: Towards a more equitable ethnomusicology” Ethnomusicology Forum 21(2): 125-140.
• Magnimind. (2019, July 20). “Immersive Virtual Reality AI and Its Near-Coming Effects” Medium. Retrieved from https://becominghuman.ai/immersive-virtual-reality-ai-and-its-near-coming-effects-40f530efe7e0
• Magnusson, Thor. (2019). Sonic writing: technologies of material, symbolic, and signal inscriptions. USA: Bloomsbury Publishing.
• Mahmoud, Ali B. (2023). “The Metaverse and Web 3.0: Revolutionising Consumption and Communication for the Future” Handbook of Research on Consumer Behavioral Analytics in Metaverse and the Adoption of a Virtual World, Ed. Pantea Keikhosrokiani: pp. 322-345. IGI Global.
• Marr, Bernard. (2021, August 12). Augmented and Virtual Reality in Social Media”. Retrieved from https://www.linkedin.com/pulse/augmented-virtual-reality-social-media-bernard-marr/
• Martin, Tania Josephine; Esteve-Faubel, José María; and Esteve-Faubel, Rosa Pilar. (2021). “Developing intercultural citizenship competences in higher education by using a literary excerpt in an English as a Foreign Language (EFL) context” Intercultural Education, 32(6): 649-666.
• Merkin, Rebecca S. (2017). “Cross‐cultural communication theory and research, overview” The International Encyclopedia of Intercultural Communication, 1-10.
• Negi, Naveen; Mohan, Arvind; Usman, Musliadi Bin; and Tabuena, Almighty Cortezo. (2023). Challenges of skill development of workers in msme sector: An empirical study of training organisations. European Economic Letters (EEL), 13(3): 89-94.
• Nickerson, Charlotte. (2023). “Latour's Actor Network Theory”. Simply Sociology. https://simplysociology.com/Actor-Network-Theory.html#:~:text=Actor%20network%20theory%2C%20usually%20abbreviated%20as%20ANT%2C%20aims,theory%2C%20sociological%20and%20technological%20factors%20hold%20equal%20weight.
• Pagano, Tiago P.; Loureiro, Rafael B.; Lisboa, Fernanda V. N.; Peixoto, Rodrigo M.; Guimarães, Guilherme A. S.; Cruz, Gustavo O. R.; Araujo, Maira M.; Santos, Lucas L.; Cruz, Marco A. S.; and Oliveira, Ewerton L. S. (2023). Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods. Big data and cognitive computing, 7(1), 15.
• Panteli, Maria; Benetos, Emmanouil; and Dixon, Simon. (2018).“A review of manual and computational approaches for the study of world music corpora”. Journal of New Music Research, 47(2): 176-189.
• Prasad, Pavithra; Roy, Jeff. (2017). “Ethnomusicology and Performance Studies: Towards Interdisciplinary Futures of Indian Classical Music”. MUSICultures, 44(1): 187-209.
• Price, Sarah M. (2017). “Risk and reward in classical music concert attendance: Investigating the engagement of ‘art’and ‘entertainment’audiences with a regional symphony orchestra in the UK” PhD Dissertation, University of Sheffield, Shefield: UK.
• Rabbi, Fazle; Ayaz, Muhammad; Dayupay, Johnry P.; Oyebode, Oluwadare Joshua; Gido, Nathaniel G., Adhikari, Nirmal; Tabuena, Almighty Cortezo; Ajibade, Samuel-Soma M.; and Bassey, Mbiatke Anthony. (2022, July). “Gaussian map to improve firefly algorithm performance” 2022 IEEE 13th Control and System Graduate Research Colloquium (ICSGRC) (pp. 88-92). IEEE.
• Rice, Timothy. (2017). Modeling ethnomusicology. New York: Oxford University Press.
• Roads, Curtis. (1980). “Artificial intelligence and music” Computer Music Journal, 4(2): 13-25.
• Roche, Jenny; Burridge, Stephanie. (2022). Choreography: The Basics. London: Routledge.
• Rochina-Chisag, Angel Geovanny; Tabuena, Almighty Cortezo. (2022). “Online learning as an alternative learning modality in Ecuador’s education institutions amidst crises and outbreaks: A SWOT analysis” Journal of Learning for Development, 9(3): 475-491.
• Seeger, Anthony. (2004). Why Suyá sing: a musical anthropology of an Amazonian people. Urbana: University of Illinois Press.
• Sleeper, Morgan Thomas. (2018). Musicolinguistics: New methodologies for integrating musical and linguistic data. California: University of California, Santa Barbara.
• Silverman, Marissa. (2008). “A performer's creative processes: Implications for teaching and learning musical interpretation” Music Education Research, 10(2): 249-269.
• Tabuena, Almighty Cortezo. (2021). “Carabo-Cone, Dalcroze, Kodály, and Orff Schulwerk methods: An explanatory synthesis of teaching strategies in music education” International Journal of Asian Education, 2(1): 9-16.
• Tabuena, Almighty Cortezo. (2020). “A literature review on the construction of a teacher-made assessment tool in music listening response competency for junior high school K to 12 music curriculum” The International Journal of Humanities & Social Studies, 8(4): 157-163.
• Tabuena, Almighty Cortezo; Hilario, Yvon Mae Cervantes. (2021). “Research data analysis methods in addressing the K-12 learning competency on data analysis procedures among senior high school research courses” International Journal of Recent Research and Applied Studies, 8(3): 1.
• Tabuena, Almighty Cortezo; Hilario, Yvon Mae Cervantes; and Buenaflor, Mhelmafa Pasagui. (2021). “Overview and exemplar components of the research methodology on the research writing process for senior high school students” International Journal of Trend in Scientific Research and Development, 5(3): 117-126.
• Tabuena, Almighty Cortezo; Morales, Glinore Santiago; and Perez, Mary Leigh Ann Corpus. (2022a). “The value of music education in the development of internationally competent students” Journal of Humanities, Music and Dance (JHMD) 2(02): 13-18.
• Tabuena, Almighty Cortezo; Morales, Glinore Santiago; and Perez, Mary Leigh Ann Corpus. (2022b). “Music assessment techniques for evaluating the students’ musical learning and performance in the Philippine k-12 basic education curriculum” Harmonia: Journal of Arts Research and Education, 21(2): 192-203.
• Tabuena, Almighty C.; Vıllareal, Elena G. (2024). “Usefulness and challenges of clustered self-directed learning modules in entrepreneurship for senior high school distance learning” Turkish Online Journal of Distance Education, 25(1): 155-178.
• Tofalvy, Tamas; Koltai, Júlia. (2021). “Splendid Isolation”: The reproduction of music industry inequalities in Spotify’s recommendation system. new media & society, 14614448211022161.
• Treloyn, Sally; Emberly, Andrea. (2013). “Sustaining traditions: Ethnomusicological collections, access and sustainability in Australia”. Musicology Australia, 35(2): 159-177.
• Trinity College Dublin. (2021, August 3). “The music of silence: Imagining a song triggers similar brain activity to moments of mid-music silence” ScienceDaily. www.sciencedaily.com/releases/2021/08/210803105600.htm
• Widmer, Gerhard. (2003). “Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries” Artificial Intelligence, 146(2): 129-148.
• Yang, Luwei. (2017). “Computational modelling and analysis of vibrato and portamento in expressive music performance” PhD Dissertation, Queen Mary University of London, London: UK.
• Yin, Betty, Samuel Bailey, Emma Hu, Milinda Jayarekera, Alex Shaw, and Burkhard C. Wünsche Yin, Betty; Bailey, Samuel; Hu, Emma; Jayarekera, Milinda; Shaw, Alex; and Wünsche, Burkhard C. (2021, February). “Tour de Tune 2-Auditory-Game-Motor Synchronisation with Music Tempo in an Immersive Virtual Reality Exergame” 2021 Australasian Computer Science Week Multiconference (pp. 1-10).
• Zhang, Xiangxin; Ding, Bowen; Chen, Liang; Huang, Xiangdong; Zhang, Kejian; Wang, Zhexin; and Yao, Feng. (2022). “Primary pulmonary choriocarcinoma in male: report a case with genetic testing and review of the literature” Translational Cancer Research, 11(6): 1844.