Abstract

The reconstruction of a pipe organ involves determining the blowing pressure. The lack of information about the pressure value may even result in irreversible damage to the pipes, as the adjustment of the sound parameters that depend on the pressure requires changing the physical structure of the pipes. In this paper, we provide a methodology for determining the blowing pressure in a pipe organ, and present a formula describing the air pressure in the pipe foot, depending only on the height of the pipe’s cut-up and the fundamental frequency. We apply machine learning to determine the blowing pressure, based on the parameters of only a percentage of pipes. We found that the height of the cut-up and the fundamental frequency allow determining the blowing pressure. The more pipes, the higher the accuracy, but even 10% of pipes can be sufficient.

Recommended Citation

Węgrzyn, D., Wrzeciono, P. & Wieczorkowska, A. (2024). The Reconstruction of Blowing Pressure in Pipe Organ Using Machine Learning. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.113

Paper Type

Poster

DOI

10.62036/ISD.2024.113

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References_DOI_ISD.2024.113.pdf (99 kB)
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The Reconstruction of Blowing Pressure in Pipe Organ Using Machine Learning

The reconstruction of a pipe organ involves determining the blowing pressure. The lack of information about the pressure value may even result in irreversible damage to the pipes, as the adjustment of the sound parameters that depend on the pressure requires changing the physical structure of the pipes. In this paper, we provide a methodology for determining the blowing pressure in a pipe organ, and present a formula describing the air pressure in the pipe foot, depending only on the height of the pipe’s cut-up and the fundamental frequency. We apply machine learning to determine the blowing pressure, based on the parameters of only a percentage of pipes. We found that the height of the cut-up and the fundamental frequency allow determining the blowing pressure. The more pipes, the higher the accuracy, but even 10% of pipes can be sufficient.