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Analysis of Evaluation in Artificial Intelligence Music

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DOI: 10.23977/jaip.2023.060802 | Downloads: 41 | Views: 654

Author(s)

Xuan Zhou 1

Affiliation(s)

1 Baoji University of Arts and Sciences, Baoji, Shaanxi, China

Corresponding Author

Xuan Zhou

ABSTRACT

The widespread application of artificial intelligence (AI) technology has profoundly changed various industries, including the music field. The application of AI technology in music creation and evaluation has already begun to show its impact, and this trend is expected to drive the development of the music industry. Specifically, AI technology can generate new music compositions by learning and simulating the styles and techniques of musicians in music creation. In terms of music evaluation, AI technology can objectively evaluate the quality and style of music compositions by analyzing elements such as pitch, rhythm, and chords, and provide objective evaluations of music compositions.

KEYWORDS

Artificial intelligence; music composition; music evaluation

CITE THIS PAPER

Xuan Zhou, Analysis of Evaluation in Artificial Intelligence Music. Journal of Artificial Intelligence Practice (2023) Vol. 6: 6-11. DOI: http://dx.doi.org/10.23977/jaip.2023.060802.

REFERENCES

[1] Miranda, E. R. Artificial Intelligence and Music: Open Problems and Future Directions [J]. Journal of New Music Research, 46(4), 333-341.2017.
[2] Yang, Y., Kang, S., Kim, Y., & Lee, S. Music generation using deep learning: A review [J]. IEEE Access, 6, 47697-47715.2018.
[3] Papadopoulos, H., & Wiggins, G. A. Artificial intelligence and creativity: an interdisciplinary approach [M]. Springer.2019. 
[4] Maestre, E., Sordo, M., & Gomez, E. (2017). Automatic music generation: challenges and perspectives. IEEE Multimedia [J]. 24(2). 82-86. 
[5] Roberts, A., Engel, J., Raffel, C., Hawthorne, C., & Eck, D. A hierarchical latent vector model for learning long-term structure in music[J]. arXiv preprint arXiv:1803.05428. 
[6] Liang, Y., Wang, Y., & Wang, J. (2017). Automatic music genre classification via convolutional neural network[J]. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 221-225). IEEE. 
[7] Sturm, B. L. (2018). Evaluating creative AI: Methods, metrics, and motivations [J]. arXiv preprint arXiv:1812.02353.

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