Os Potenciais Impactos Ético Legais da Aplicação de Modelos Generativos de Áudio na Música
Resumo
Com os recentes avanços em algoritmos generativos, o uso de modelos para geração de áudio se torna cada vez mais comum trazendo consigo diversas implicações, em particular, no campo da música. No entanto, poucas pesquisas discutem tais impactos, de forma que a literatura carece de estudos que discorram sobre essa questão. Este artigo discute as principais consequências éticas do crescente uso de modelos generativos de áudio na indústria musical. O foco está nos possíveis impactos negativos, como o perigo de apagamento de gêneros musicais marginais, o esgotamento de criatividade, e os impactos de modelos generativos no meio ambiente, além de potenciais implicações ético-legais. Recomendações são feitas para evitar, ou ao menos mitigar, os potenciais riscos e danos discutidos.Referências
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Briot, J.-P. (2020). From artificial neural networks to deep learning for music generation: history, concepts and trends. Neural Comput & Applic, 33:39–65.
Chemla-Romeu-Santos, A. and Esling, P. (2022). Challenges in creative generative models for music: a divergence maximization perspective.
Civit, M., Civit-Masot, J., Cuadrado, F., and Escalona, M. J. (2022). A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends. Expert Systems with Applications, 209:118–190.
Douwes, C., Esling, P., and Briot, J. (2021). A multi-objective approach for sustainable generative audio models. CoRR, abs/2107.02621.
Frid, E., Gomes, C., and Jin, Z. (2020). Music creation by example. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20, page 1–13, New York, NY, USA. Association for Computing Machinery.
Huang, C. A., Cooijmans, T., Roberts, A., Courville, A. C., and Eck, D. (2019). Counterpoint by convolution. CoRR, abs/1903.07227.
Huang, C. A., Koops, H. V., Newton-Rex, E., Dinculescu, M., and Cai, C. J. (2020). AI song contest: Human-AI co-creation in songwriting. CoRR, abs/2010.05388.
Huang, C. A., Vaswani, A., Uszkoreit, J., Shazeer, N., Hawthorne, C., Dai, A. M., Hoffman, M. D., and Eck, D. (2018). An improved relative self-attention mechanism for transformer with application to music generation. CoRR, abs/1809.04281.
Menabrea, L. F. and Lovelace, A. (1842). Sketch of the analytical engine invented by Charles Babbage.
Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. CoRR, abs/1906.02243.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. CoRR, abs/1706.03762.
Verdecchia, R., Sallou, J., and Cruz, L. (2023). A systematic review of green ai. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13.
Wen, Y.-W. and Ting, C.-K. (2020). Composing bossa nova by evolutionary computation. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8.
Publicado
21/07/2024
Como Citar
D’ASSUMPÇÃO, João Pedro de Matos; ALMEIDA, Gilberto Martins de; FERRO, Mariza.
Os Potenciais Impactos Ético Legais da Aplicação de Modelos Generativos de Áudio na Música. In: WORKSHOP SOBRE AS IMPLICAÇÕES DA COMPUTAÇÃO NA SOCIEDADE (WICS), 5. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 80-88.
ISSN 2763-8707.
DOI: https://doi.org/10.5753/wics.2024.2057.
