Artificial Intelligence for Music Production (IAPM)
Pedagogy and Teaching through Innovative Technological Strategies.
DOI:
https://doi.org/10.59343/yuyay.v3i1.59Keywords:
Education, music production, artificial intelligence, educational innovationAbstract
Since its inception, music production has had a close relationship with technology; it exists thanks to it. Therefore, technological changes have an impact on the ways music is recorded and on the audio aesthetics. Now, how does emerging artificial intelligence (AI) operate in music production? What alternatives do institutions and professionals dedicated to audio teaching have in the face of the consolidation of artificial intelligence for music production (IAPM)? The objective of this essay is to analyze the impact of AI from the intersection of education, music production, and new technologies. An exhaustive study was conducted, which included bibliographic review, interviews with audio specialists, and the application of some IAPM technologies. From this approach, we have sought to understand how technological strategies are transforming the teaching of music production. Among the results presented, it is revealed that the discussion about music, computers, and artificial intelligence has been going on for almost seventy years; however, it is currently an emerging field in constant evolution. The role of institutions that teach this discipline and the integration of artificial intelligence for music production into their academic programs have been highlighted. In this way, the aim is to enhance the sustainability and effectiveness of teaching for music production. Similarly, the new role of the teacher is emphasized, who will be able to act as a tutor, curator, and advisor to students through the use of IAPM.
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References
Avdeeff, M. (2019). Artificial Intelligence & Popular Music: SKYGGE, Flow Machines, and the Audio Uncanny Valley. Arts, 8 (4), 130-151. https://doi.org/10.3390/arts8040130
Barragán Becerra, J., Hernández, N. E. y Medina Castro, A. (2017). Validación de guías de autoaprendizaje en simulación clínica para estudiantes de enfermería. Revista Cuidarte, 8 (2), 1582-1590. https://doi.org/10.15649/cuidarte.v8i2.377
Born, G. (1995). Rationalizing Culture. IRCAM, Boulez, and the Institutionalization of the Musical Avant-Garde. University of California Press.
Born, G., Morris, J., Díaz, F. y Anderson, A. (2021). Artificial Intelligence, Music Recomendation, and the Curation of Culture. Universidad de Toronto.
Bowen, O. (2021). Sociocultural and Design Perspectives on AI-Based Music Production: Why Do We Make Music and What Changes if AI Makes It for Us? en E.R. Miranda (Ed.), Handbook of Artificial Intelligence for Music (pp. 1-20). Springer. https://doi.org/10.1007/978-3-030-72116-9_3
Burgess, R.J. (2013). The Art of Music Production: The Theory and Practice. Oxford University Press.
Buning, M. de C. (2018). Artificial Intelligence and the Creative Industry: New Challenges for the EU Paradigm for Art and Technology en W. Barfield y U. Pagallo (Eds.), Research handbook on the law of artificial intelligence (pp. 511-535). Edward Elgar Publishing. https://hdl.handle.net/1814/70217
Caballero Parra, C.A. (2023). La producción musical en Colombia en las décadas de 1960 y 1970. Formas de registro y estética sonora de la música tropical colombiana [Tesis de doctorado, Universidad Politécnica de Valencia]. https://riunet.upv.es/handle/10251/192511
Cartwright, M. y Pardo, B. (2014). SynthAssist: an audio synthesizer programmed with vocal imitation. Proceedings of the 22nd ACM International Conference on Multimedia, pp. 741-742. https://doi.org/10.1145/2647868.2654880
Cetta, P. (2018). Fundamentos de composición musical asistida por computadora en el entorno de programación OpenMusic. Universidad Católica Argentina.
Cheung-Ruiz, M. y Pérez-Valero, L. (2020). Producción musical. Pedagogía e investigación en artes. UArtes Ediciones.
Clark, E., Ross, A.S., Tan, C., Ji, Yi., y Smith, N.A. (2018). Creative writing with a machine in the loop: Case studies on slogans and stories. 23rd International Conference on Intelligent User Interfaces (IUI). https://doi.org/10.1145/3172944.3172983
Deruty, E., Grachten, M., Lattner, J.N., Aouameur, G. (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://transactions.ismir.net/articles/10.5334/tismir.100
Di Cione, L. (2023). Musicología de la producción fonográfica: las operaciones técnico-discursivas en el estudio de grabación analógica y las poéticas sonoras del rock en Argentina [Tesis de doctorado, Universidad de Buenos Aires]. https://repositoriosdigitales.mincyt.gob.ar/
Dirst, M. (2012). Engaging Bach: The Keyboard Legacy from Marpurg to Mendelssohn. Cambridge University Press.
Dugan, D. (1975). Automatic Microphone Mixing. Journal of the Audio Engineering Society 23, 442-449.
Espiga, P. (2020). La construcción de la imagen del estudio de grabación tradicional. Etno: Cuadernos de Etnomusicología, 15 (2), 226-246. https://www.sibetrans.com/
Flores-Vivar, J. M. y García Peñalvo, F. J. (2023). Reflexiones sobre la ética, potencialidades y retos de la Inteligencia Artificial en el marco de la Educación de Calidad (ODS4). Comunicar, 74 (31). https://doi.org/10.3916/C74-2023-03
Giotti, A. (2021). Artificial intelligence for music composition en E.R. Miranda (Ed.), Handbook of Artificial Intelligence for Music (pp. 53-73). Springer Link. https://doi.org/10.1007/978-3-030-72116-9_3
Gómez Jerez, A.M. (2021). La capacidad creativa en los sistemas de inteligencia artificial y sus consideraciones en el derecho de autor. La Propiedad Inmaterial, 31, 283-297. https://doi.org/10.18601/16571959.n31.11
González Álvarez, P. (2018). Diseño de una plataforma virtual de autoaprendizaje de la escritura académica: fundamentación teórica y decisiones pedagógicas en la Universidad de Chile. Álabe 17, 1-17. https://doi.org/10.15645/Alabe2018.17.7
González Sánchez, J. L., Villota García, F. R., Moscoso Parra, A. E., Garces Calva, S. W., Bazurto Arévalo, B. M. (2023). Aplicación de la Inteligencia Artificial en la Educación Superior. Revista Científica. Dominio de las Ciencias, 9 (3), 1097-1108. https://doi.org/10.23857/dc.v9i3.3488
Grachten, M., Lattner, S., y Deruty, E. (2020). Bass-net: A variational gated autoencoder for conditional generation of bass guitar tracks with learned inetractive control. Applied Science, 18 (10). https://doi.org/10.3390/app10186627
Gunkel, D.J. (2008). Rethinking the Digital Remix: Mash-Ups and the Metaphysics of Sound Recording. Popular Music and Society, 31, 489-510. https://doi.org/10.1080/03007760802053211
Hatschek, K. y Wells, V.A. (2018). Historical Dictionary of the American Music Industry. Rowman & Littlefield.
Herndon, H. (2019). Proto. 4AD.
Hiller, L.A. y Isaacson, L. (1959). Experimental Music: Composition with an Electronic Computer. McGraw-Hill.
Jillings, N. y Stables, R. (2017). Automatic Masking Reduction in Balance Mixes Using Evolutionary Computing. Audio Engineering Society Convention 43. Audio Engineering Society.
Juan de Dios Cuartas, M.A. (2016). La figura del productor musical en España. Propuestas metodológicas para un análisis musicológico [Tesis de doctorado, Universidad de Oviedo].
Knotts, S. y Collins, N. (2021). AI-Lectronica: Music AI in clubs and studio production en Miranda, E.R. (Ed.), Handbook of Artificial Intelligence for Music (pp. 849-877). Springer. https://doi.org/10.1007/978-3-030-72116-9_3
Lacruz Mantecón, M. (2021). Inteligencia Artificial y derecho de autor. Editorial Reus.
Lauber-Rönsberg, A. y Hetmank, S. (2019). The concept of authorship and inventorship under pressure: Does artificial intelligence shift paradigms? Journal Intellectual Property Law & Practice, 14, 570-579. https://doi.org/10.1093/jiplp/jpz061
Lázaro, N. (2011). Tendencias pedagógicas en centros de autoaprendizaje de Alemania, Suiza, Hong Kong y España. Universidad Nacional de Educación a Distancia.
Miranda, E., ed. (2000). Readings in Music and Artificial Intelligence. Harwood.
Moffat, D. y Sandler, M.B. (2019). Approaches in Intelligent Music Production. Arts, 8 (4), 125-129. https://doi.org/10.3390/arts8040125
Moylan, W. (2020). Recording Analysis. How the Record Shapes the Song. Routledge.
Novotny, A. (2018). A Collection of Art: The Ghost Writer. Create Space.
Pardo, B., Cartwright, M., Seetharaman, P., y Kim, B. (2019). Learning to Build Natural Audio Production Interfaces. Arts, 8 (3), 110-131. https://doi.org/10.3390/arts8030110
Paterson, J., Toulson, R., y Hepworth-Sawyer, R. (2019). User-Influenced / Machine-Controlled Playback: The VariPlay Music App Format for Interactive Recorded Music. Arts, 8 (3), 112-129. https://doi.org/10.3390/arts8030112
Pérez-Valero, L. (2022). La producción discográfica de Xavier
Cugat (1933-1950) [Tesis de doctorado, Pontificia Universidad Católica Argentina]. https://repositorio.uca.edu.ar/handle/123456789/14389
Piantanida, P., y Vega, L.R. (2021). Information bottleneck and representation learning en Rodriguez, M.R.D. y Eldar, Y.C. (Eds.), Information Theoretic Methods in Data Science (pp. 330-358). Cambridge University Press. https://doi.org/10.1017/9781108616799.012
Reje, A. (2022). Ethical Risk Analysis of the Use of AI Music Production [Tesis de Maestría, KTH Royal Institute of Technology]. https://kth.diva-portal.org/
Road, C. (1980). Artificial Intelligence and Music. Computer Music Journal 4, 13-25.
Ros-Fábregas, E. (2023). Musicología en la era de la inteligencia artificial (IA). Anuario Musical, (78), 7-12. https://doi.org/10.3989/anuariomusical.2023.78.01
Russell, S. y Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Prentice-Hall.
Sanz Mendioroz, M. (2023). Autoría como elemento principal de los derechos de autor en el ámbito de la Inteligencia Artificial (IA). [Tesis de grado. Comillas Universidad Pontificia]. https://repositorio.comillas.edu/
Seabrook, J. (29 de enero de 2024). Inside the Music Industry’s High-States A.I. Experiments. The New Yorker Daily. https://www.newyorker.com/magazine/2024/02/05/inside-the-music-industrys-high-stakes-ai-experiments
Schedl, M., Yang, Yi-Hsuan y Herrera-Boyer, P. (2016). Introduction to Intelligent Music Systems and Applications. ACM Transactions on Intelligent Systems and Technology, 8 (17), 1-8. https://doi.org/10.1145/2991468
Sheridan, T. B. y Verplank, W. L. (1978). Human and Computer Control of Undersea Teleoperators. Technical Report. Massachusetts Inst of Teach Cambridge Man-Machine Systems Lab.
Skygge (2018). Hello World. Sony Music.
Southerm, T. (2018). I AM AI. Independiente.
Stypullkowski, K. (2020). Los estudios para piano de Teobaldo Power (1848-1884) en el desarrollo de la escuela pianística en España [Tesis de Fin de Máster, Universidad de Valladolid]. https://uvadoc.uva.es/handle/10324/45855
Sturm, B. L., Iglesias, M., Tal, O. B., Mixon, M. y Gómez, E. (2019). Artificial Intelligence and Music: Open Questions of Copyright Law and Engineering Praxis. Arts, 8 (3), 115-119. https://doi.org/10.3390/arts8030115
Terrones Rodríguez, A. L. y Rocha Benardi, M. (2024). El valor de la ética aplicada en los estudios de ingeniería en un horizonte de inteligencia artificial confiable. Sophia. Colección de Filosofía de la Educación, 36, 221-245. https://doi.org/10.17163/soph.n36.2024.07
Tomalá de la Cruz, M.A., Mascaró Benítez, E.M., Carrasco Cachinelli, C.G. y Aroni Caicedo, E.V. (2023). Incidencias de la inteligencia artificial en la educación. Recimundo. Revista Científica Mundo de la Investigación y el Conocimiento, 7 (2), 238-251.
Tsiros, A. y Palladini, A. (2020). Towards a Human-Centric Design Framework for AI Assisted Music Production. NIME’20, 399-404.
Zagorski-Thomas, S. (2014). The Musicology of Record Production. Cambridge University Press.
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