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Recognition of the Flue Pipe Type Using Deep Learning

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Part of the Studies in Computational Intelligence book series (SCI,volume 949)


This paper presents the usage of deep learning in flue pipe type recognition. The main thesis is the possibility of recognizing the type of labium based on the sound generated by the flue pipe. For the purpose of our work, we prepared a large data set of high-quality recordings, carried out in an organbuilder’s workshop. Very high accuracy has been achieved in our experiments on these data using Artificial Neural Networks (ANN), trained to recognize the details of the pipe mouth construction. The organbuilders claim that they can distinguish the pipe mouth type only by hearing it, and this is why we decided to verify if it is possible to train ANN to recognize the details of the organ pipe, as this confirms a possibility that a human sense of hearing may be trained as well. In the future, the usage of deep learning in the recognition of pipe sound parameters may be used in the voicing of the pipe organ and the selection of appropriate parameters of pipes to obtain the desired timbre.


Partially supported by research funds sponsored by the Ministry of Science and Higher Education in Poland.

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Special thanks to the organbuilder Władysław Cepka for his invaluable help and providing the workshop and organ pipes for sound recording.

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Correspondence to Damian Węgrzyn .

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Węgrzyn, D., Wrzeciono, P., Wieczorkowska, A. (2021). Recognition of the Flue Pipe Type Using Deep Learning. In: Stettinger, M., Leitner, G., Felfernig, A., Ras, Z.W. (eds) Intelligent Systems in Industrial Applications. ISMIS 2020. Studies in Computational Intelligence, vol 949. Springer, Cham.

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