Inovações na Detecção de Ruídos Antropogênicos com Aprendizado de Máquina
Resumo
O ruído antropogênico é um dos principais desafios para a conservação da vida selvagem, mascarando sinais vitais e alterando ecossistemas. O monitoramento acústico passivo, impulsionado pelo aprendizado de máquina, tornou-se uma ferramenta essencial para estudar esses impactos. Este artigo analisa a evolução das técnicas de detecção de ruído antropogênico utilizando a teoria da inovação de Joseph Schumpeter. Argumentamos que a transição de algoritmos clássicos para redes neurais convolucionais representa um processo de ”destruição criadora”, onde uma nova tecnologia não apenas melhora, mas substitui a anterior, redefinindo as fronteiras da pesquisa em bioacústica.
Palavras-chave:
Ruídos antropogênicos, Aprendizado de máquina, Bioacústica, Monitoramento acústico passivo, Teoria da inovação
Referências
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Dahlheim, M. and Castellote, M. (2016). Changes in the acoustic behavior of gray whales eschrichtius robustus in response to noise. Endangered Species Research.
Devane, D., C., H., Gartlehner, G., N.-S. B., Griebler, U., Affengruber, L., Saif-Ur-Rahman, K. M., and C., G. (2024). Key concepts in rapid reviews: an overview. Journal of Clinical Epidemiology, 175:111518. Epub 2024 Sep 6.
Grant, M. J. and Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26(2):91–108.
Higham, V., Deal, N. D. S., Chan, Y. K., Chanin, C., Davine, E., Gibbings, G., Keating, R., Kennedy, M., Reilly, N., Symons, T., Vran, K., and Chapple, D. G. (2021). Traffic noise drives an immediate increase in call pitch in an urban frog. Journal of Zoology.
Huang, C.-J., Yang, Y.-J., Yang, D.-X., and Chen, Y.-J. (2009). Frog classification using machine learning techniques. Expert Systems with Applications, 36(2, Part 2):3737–3743.
Kok, A. C. M., Berkhout, B. W., Carlson, N. V., Evans, N. P., Khan, N., Potvin, D. A., Radford, A. N., Sebire, M., Shafiei Sabet, S., Shannon, G., and Wascher, C. A. F. (2023). How chronic anthropogenic noise can affect wildlife communities. Frontiers in Ecology and Evolution, 11. Section Behavioral and Evolutionary Ecology.
Lampert, T. A. and O’Keefe, S. E. (2010). A survey of spectrogram track detection algorithms. Applied Acoustics, 71:87–100.
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.
Morandin, J. L. P. L., Silva, M. C. d., and Moura, A. M. M. d. (2023). As patentes e o desenvolvimento tecnológico no contexto da ciência aberta: perspectivas da influência do sigilo informacional e da pesquisa proprietária. RDBCI: Revista Digital de Biblioteconomia e Ciência da Informação, 21(00):e023019.
Ranjard, L., Reed, B. S., Landers, T. J., Rayner, M. J., Friesen, M. R., Sagar, R. L., and Dunphy, B. J. (2017). Matlabhtk: a simple interface for bioacoustic analyses using hidden markov models. Methods in Ecology and Evolution, 8:615–621.
Ren, Y., Johnson, M. T., Clemins, P. J., Darre, M., Glaeser, S. S., Osiejuk, T. S., and Out-Nyarko, E. (2009). A framework for bioacoustic vocalization analysis using hidden markov models. Algorithms, 2(4):1410–1428.
Schumpeter, J. (1934). The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle. Harvard University Press, Cambridge.
Schumpeter, J. A. (1911). The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle. Harvard University Press.
Schumpeter, J. A. (1942). Capitalism, Socialism and Democracy. Harper Brothers.
Slabbekoorn, H. and Peet, M. (2003). Ecology: Birds sing at a higher pitch in urban noise. Nature, 424(6946):267.
Stowell, D. (2022). Computational bioacoustics with deep learning: a review and roadmap. PeerJ, 10:e13152.
Stowell, D. and Plumbley, M. D. (2014). Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ, 2:e488.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need.
Wijayasingha, L. and Stankovic, J. A. (2021). Robustness to noise for speech emotion classification using cnns and attention mechanisms. Smart Health, 19:100165.
Zhang, C., Sainath, T. N., Wu, Y., et al. (2018). Deep learning for environmentally robust speech recognition: An overview of recent developments. IEEE Signal Processing Magazine, 35(3):114–126.
Dahlheim, M. and Castellote, M. (2016). Changes in the acoustic behavior of gray whales eschrichtius robustus in response to noise. Endangered Species Research.
Devane, D., C., H., Gartlehner, G., N.-S. B., Griebler, U., Affengruber, L., Saif-Ur-Rahman, K. M., and C., G. (2024). Key concepts in rapid reviews: an overview. Journal of Clinical Epidemiology, 175:111518. Epub 2024 Sep 6.
Grant, M. J. and Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26(2):91–108.
Higham, V., Deal, N. D. S., Chan, Y. K., Chanin, C., Davine, E., Gibbings, G., Keating, R., Kennedy, M., Reilly, N., Symons, T., Vran, K., and Chapple, D. G. (2021). Traffic noise drives an immediate increase in call pitch in an urban frog. Journal of Zoology.
Huang, C.-J., Yang, Y.-J., Yang, D.-X., and Chen, Y.-J. (2009). Frog classification using machine learning techniques. Expert Systems with Applications, 36(2, Part 2):3737–3743.
Kok, A. C. M., Berkhout, B. W., Carlson, N. V., Evans, N. P., Khan, N., Potvin, D. A., Radford, A. N., Sebire, M., Shafiei Sabet, S., Shannon, G., and Wascher, C. A. F. (2023). How chronic anthropogenic noise can affect wildlife communities. Frontiers in Ecology and Evolution, 11. Section Behavioral and Evolutionary Ecology.
Lampert, T. A. and O’Keefe, S. E. (2010). A survey of spectrogram track detection algorithms. Applied Acoustics, 71:87–100.
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.
Morandin, J. L. P. L., Silva, M. C. d., and Moura, A. M. M. d. (2023). As patentes e o desenvolvimento tecnológico no contexto da ciência aberta: perspectivas da influência do sigilo informacional e da pesquisa proprietária. RDBCI: Revista Digital de Biblioteconomia e Ciência da Informação, 21(00):e023019.
Ranjard, L., Reed, B. S., Landers, T. J., Rayner, M. J., Friesen, M. R., Sagar, R. L., and Dunphy, B. J. (2017). Matlabhtk: a simple interface for bioacoustic analyses using hidden markov models. Methods in Ecology and Evolution, 8:615–621.
Ren, Y., Johnson, M. T., Clemins, P. J., Darre, M., Glaeser, S. S., Osiejuk, T. S., and Out-Nyarko, E. (2009). A framework for bioacoustic vocalization analysis using hidden markov models. Algorithms, 2(4):1410–1428.
Schumpeter, J. (1934). The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle. Harvard University Press, Cambridge.
Schumpeter, J. A. (1911). The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle. Harvard University Press.
Schumpeter, J. A. (1942). Capitalism, Socialism and Democracy. Harper Brothers.
Slabbekoorn, H. and Peet, M. (2003). Ecology: Birds sing at a higher pitch in urban noise. Nature, 424(6946):267.
Stowell, D. (2022). Computational bioacoustics with deep learning: a review and roadmap. PeerJ, 10:e13152.
Stowell, D. and Plumbley, M. D. (2014). Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ, 2:e488.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need.
Wijayasingha, L. and Stankovic, J. A. (2021). Robustness to noise for speech emotion classification using cnns and attention mechanisms. Smart Health, 19:100165.
Zhang, C., Sainath, T. N., Wu, Y., et al. (2018). Deep learning for environmentally robust speech recognition: An overview of recent developments. IEEE Signal Processing Magazine, 35(3):114–126.
Publicado
12/11/2025
Como Citar
AHAD, Felipe R.; FIGUEIREDO, Josiel M.; C. JUNIOR, Alvaro S.; OLIVEIRA, Allan G..
Inovações na Detecção de Ruídos Antropogênicos com Aprendizado de Máquina. In: ESCOLA REGIONAL DE INFORMÁTICA DE MATO GROSSO (ERI-MT), 14. , 2025, Pontes e Lacerda/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 346-354.
ISSN 2447-5386.
DOI: https://doi.org/10.5753/eri-mt.2025.17036.
