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.

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Publication Details

Journal
Proceedings of the International Conference on Information Systems Development
Published
2024-09-09
DOI
https://doi.org/10.62036/isd.2024.113
Primary Topic
Vibration and Dynamic Analysis
Type
article
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article

The Reconstruction of Blowing Pressure in Pipe Organ Using Machine Learning

Damian Węgrzyn, Alicja Wieczorkowska, Piotr Wrzeciono
Proceedings of the International Conference on Information Systems Development
Vibration and Dynamic Analysis
article

The Reconstruction of Blowing Pressure in Pipe Organ Using Machine Learning

Damian Węgrzyn, Alicja Wieczorkowska, Piotr Wrzeciono
article en

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.

Proceedings of the International Conference on Information Systems Development
Warsaw University of Life Sciences (PL), Polish-Japanese Academy of Information Technology (PL)
Openalex Percentile: Top 84%
Vibration and Dynamic Analysis
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