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All issues Volume 8 (2024) Acta Acust., 8 (2024) 65 References
Issue
Acta Acust.
Volume 8, 2024
Topical Issue - Musical Acoustics: Latest Advances in Analytical, Numerical and Experimental Methods Tackling Complex Phenomena in Musical Instruments
Article Number 65
Number of page(s) 12
DOI https://doi.org/10.1051/aacus/2024042
Published online 20 November 2024
  1. M. Campbell, J. Gilbert, A. Myers: The science of brass instruments, Springer International Publishing, New York City, USA, 2021. [CrossRef] [Google Scholar]
  2. J.S. Cullen, J. Gilbert, D.M. Campbell: Brass instruments: linear stability analysis and experiments with an artificial mouth, Acta Acustica 86 (2000) 704–724. [Google Scholar]
  3. L. Velut, C. Vergez, J. Gilbert, M. Djahanbani: How well can linear stability analysis predict the behaviour of an outward-striking valve brass instrument model? Acta Acustica united with Acustica 103 (2017) 132–148. [CrossRef] [Google Scholar]
  4. T. Kaburagi, C. Kuroki, S. Hidaka, S. Ishikawa: Numerical method for analyzing steady-state oscillation in trumpets, Acoustical Science and Technology 44, 3 (2023) 269–280. [CrossRef] [Google Scholar]
  5. V. Fréour, L. Guillot, H. Masuda, S. Usa, E. Tominaga, Y. Tohgi, C. Vergez, B. Cochelin: Numerical continuation of a physical model of brass instruments: application to trumpet comparisons, Journal of the Acoustical Society of America 148, 2 (2020) 748–758. [CrossRef] [PubMed] [Google Scholar]
  6. V. Fréour, H. Masuda, B. Cochelin, C. Vergez. Identification of lip parameters associated to different trumpets using constrained continuation. in Proceedings of Forum Acusticum 2023, Turin, Italy, 11–15 September, 2023. [Google Scholar]
  7. J.F. Petiot, M. Roatta, V. Fréour, K. Arimoto, Contribution of machine learning and physic-based sound simulations for the charcterization of brass instruments, in: Proceedings of Forum Acusticum 2023, Turin, Italy, 11–15 September, 2023. [Google Scholar]
  8. B. Cochelin: A path following technique via an asymptotic-numerical method, Computers and Structures 53, 5 (1994) 1181–1192. [CrossRef] [Google Scholar]
  9. B. Cochelin, C. Vergez: A high order purely frequency-based harmonic balance formulation for continuation of periodic solutions, Journal of Sound and Vibration 324 (2009) 242–262. [Google Scholar]
  10. J. Gilbert, S. Maugeais, C. Vergez: Minimal blowing pressure allowing periodic oscillations in a simplified reed musical instrument model: Bouasse-Benade prescription assessed through numerical continuation, Acta Acustica 4 (2020) 27. [CrossRef] [EDP Sciences] [Google Scholar]
  11. R. Matteoli, J. Gilbert, C. Vergez, J.-P. Dalmont, S. Maugeais, S. Terrien, F. Ablitzer: Minimal blowing pressure allowing periodic oscillations in a model of bass brass instruments, Acta Acustica 5 (2021) 57. [CrossRef] [EDP Sciences] [Google Scholar]
  12. R. Matteoli, J. Gilbert, S. Terrien, J.-P. Dalmont, C. Vergez: Diversity of ghost notes in tubas, euphoniums and saxhorns, Acta Acustica 6 (2021) 32. [Google Scholar]
  13. V. Fréour, M. Mohamed, K. Arimoto, V. Emiya, B. Cochelin, C. Vergez: Machine learning applied to the prediction of trumpet bifurcation diagrams: towards a tool for trumpet designers, in: Proceedings of Forum Acusticum 2023, Turin, Italy, 11–15 September, 2023. [Google Scholar]
  14. R. Tournemenne, J.F. Petiot, B. Talgorn, M. Kokkolaras, J. Gilbert: Brass instruments design using physics-based sound simulation models and surrogate-assisted derivative-free optimization, Journal of Mechanical Design 139 (2017) 0141401-1–011401-9. [CrossRef] [Google Scholar]
  15. S. Gonzales, D. Salvi, D. Baeza, F. Antonacci, A. Sarti: A data-driven approach to violin making, Scientific Reports 11 (2021) 9455. [CrossRef] [PubMed] [Google Scholar]
  16. F. Mokdad, S. Missoum: A fully parametrized finite element model of a grand piano soundboard for sensitivity analysis of the dynamic behavior, in: Proceedingsof the ASME 2013, Portland, Oregon, USA, August 4–7, 2013. [Google Scholar]
  17. S. Adachi, M.A. Sato: Trumpet sound simulation using a two-dimensional lip vibration model, Journal of the Acoustical Society of America 99, 2 (1996) 1200–1209. [CrossRef] [Google Scholar]
  18. V. Fréour, L. Guillot, H. Masuda, C. Vergez, B. Cochelin: Parameter identification of a physical model of brass instruments by constrained continuation, Acta Acustica 6 (2022) 9. [CrossRef] [EDP Sciences] [Google Scholar]
  19. T. Chen, C. Guestrin: Xgboost: a scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August, Association for Computing Machinery, 2016, pp. 785–794. [Google Scholar]
  20. D. Berrar: Cross-validation, Encyclopedia of Bioinformatics and Computational Biology 1 (2019) 542–545. [CrossRef] [Google Scholar]
  21. H. Hotelling: Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology 24, 6 (1933) 417. [CrossRef] [Google Scholar]
  22. R. Roy, T. Kailath: Esprit: estimation of signal parameters via rotational invariance techniques, IEEE Transactions on Acoustics, Speech, and Signal Processing 37, 7 (1989) 984–995. [CrossRef] [Google Scholar]
  23. R. Tibshirani: Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society: Series B (Methodological) 58, 1 (1996) 267–288. [CrossRef] [Google Scholar]
  24. B. Efron, T. Hastie, I. Johnstone, R. Tibshirani: Least angle regression, Annals of Statistics 32, 2 (2004) 407–451. [CrossRef] [Google Scholar]
  25. A.E. Hoerl, R.W. Kennard: Ridge regression: biased estimation for nonorthogonal problems, Technometrics 12, 1 (1970) 55–67. [CrossRef] [Google Scholar]
  26. V. Vapnik: The nature of statistical learning theory Chapter 5–6, Springer Science & Business Media, Berlin, Germany, 2013. [Google Scholar]
  27. N.S. Altman: An introduction to kernel and nearest-neighbor nonparametric regression, American Statistician 46, 3 (1992) 175–185. [CrossRef] [Google Scholar]
  28. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas: Scikit-learn: machine learning in Python, Journal of Machine Learning Research 12 (2011) 2825–2830. [Google Scholar]
  29. M. Mohamed: A machine learning tool for the prediction of trumpet bifurcation diagrams – Supplementary materials, GitHub, 2024. Available at https://github.com/mimoun-mohamed-lab/Prediction-of-trumpet-descriptors. [Google Scholar]

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