Symphony of Algorithms: Evaluating the Past, Present, and Future of Artificial Intelligence in Music
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DOI:
https://doi.org/10.5281/zenodo.7973728Keywords:
Artificial intelligence, algorithms, musicAbstract
In this study, an evaluation has been made on the history, current state, and future of artificial intelligence's use in the field of music. Artificial intelligence has brought about a significant transformation in music. In the past, music production and composition processes relied on human creativity, but with advanced algorithms in artificial intelligence today, impressive results are achieved in music production. Artificial intelligence can analyze the mathematical properties of music through techniques such as big data analysis and pattern recognition. Thus, it can understand the structures, melodies, and rhythms of compositions and generate similar ones. Additionally, artificial intelligence inspires applications such as virtual instruments and musical robots that can produce sounds resembling real instruments. In the future, an increased interaction between artificial intelligence and music is expected. Artificial intelligence-based composition, music production, and performance are anticipated to further advance. However, the limitations of artificial intelligence and its ability to replace a real musical experience or human creativity remain subjects of ongoing debate. As a result, it is inevitable that artificial intelligence has a significant impact on the field of music, similar to its impact in other areas, and its importance will continue to grow in the future.
References
Al-Rikabi, A.B., Oram, A., 2020. AI and creativity: Understanding the potential impact on music creation and copyright law. Computer Law & Security Review, 37:105420.
Brøvig-Hanssen, R., Hagen, A., 2020. The future of work in the music industries: Technological and social perspectives. Nordic Journal of Media Management, 1(2): 37-56.
Choi, J.Y., Han, K., 2020. Exploring the ethical implications of AI-generated music in the music industry. Media, Culture & Society, 42(7-8):1221-1236.
Civit, M., Civit-Masot, J., Cuadrado, F., Escalona, M.J., 2022. A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends. Expert Systems with Applications, 209:118190.
Cope, D., 2005. Computer Models of Musical Creativity (First Edition). The MIT Press, London, England.
Gasser, U., Alsenoy, V.V., 2019. Regulating artificial intelligence in the music industry: Revisiting copyright law for AI-assisted music creation. International Journal of Law and Information Technology, 27(4): 349-378.
Gioti, A.M., 2020. From artificial to extended intelligence in music composition. Organised Sound, 25 (1): 25-32.
Magenta, 2023. Magenta Project, (https://magenta.tensorflow.org/), (Erişim tarihi 19.01.2023).
Jones, A., Lee, S., 2020. Artificial intelligence and music: Open questions and current perspectives. Journal of New Music Research, 49(5): 413-428.
Jukedeck, 2023. Artificially Intelligent Composer, https://www.jukedeck.com), (Erişim tarihi: 03.04.2023).
Laplante, A., Dachtera, J., 2020. Music and AI: Ethical challenges and regulatory frameworks. ACM Transactions on Management Information Systems, 11(3):1-17.
Madhiarasan, M., Louzazni, M., 2022. Analysis of artificial neural network: Architecture, types, and forecasting applications. Journal of Electrical and Computer Engineering, Article ID 5416722: 23.
McFee, B., Bertin-Mahieux, T., Ellis, D. P.W., Lanckriet, G. R.G., 2012. The million song dataset challenge. www 2012: 21st World Wide Web Conference, Congress Book, 16-20 April, France, s. 909-916.
OpenAI, 2023. Muse Net, (https://openai.com/research/musenet/), (Erişim tarihi: 20.03.2023).
Pachet, F., 2003. The continuator: Musical interaction with style. Journal of New Music Research, 32(3):333-341.
Salamon, J., Bello, J. P., 2017. Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Processing Letters, 24(3): 279-283.
Simoes, J.M., Machado, P., Rodrigues, A.C., 2019. Deep learning for expressive music generation. ARTECH 2019: 9th International Conference on Digital and Interactive Arts, Congress Book, 23 – 25 October, Portugal, s. 1-9.
Tarrant, D., 2019. Artificial intelligence and copyright: Who owns AI-generated works?. Computer Law & Security Review, 35(5): 589-601.
Zimmer, H., 2018. Composer Hans Zimmer Collaborates with Artificial Intelligence to Create Score for Blue Planet II, (https://www.bbc.co.uk/mediacentre/latestnews/2018/hans-zimmer), (Erişim tarihi: 19.01.2023).
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