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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)

Abstract

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.

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

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References

  1. Angster, J., Rusz, P., Miklos, A.: Acoustics of organ pipes and future trends in the research. Acoust. Today 1(13), 10–18 (2017)

    Google Scholar 

  2. Außerlechner, H., Trommer, T., Angster, J., Miklos, A.: Experimental jet velocity and edge tone investigations on a foot model of an organ pipe. J. Acoust. Soc. Am. 2(126), 878–886 (2009). https://doi.org/10.1121/1.3158935

    Article  Google Scholar 

  3. Deep Cognition Homepage. https://deepcognition.ai. Accessed 24 Apr 2020

  4. Herremans, D., Chuan, C.: The emergence of deep learning: new opportunities for music and audio technologies. Neural Comput. Appl. 32, 913–914 (2020). https://doi.org/10.1007/s00521-019-04166-0

    Article  Google Scholar 

  5. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  6. Hruška, V., Dlask, P.: Investigation of the sound source regions in open and closed organ pipes. Arch. Acoust. 3(44), 467–474 (2019). https://doi.org/10.24425/aoa.2019.129262

    Article  Google Scholar 

  7. Hruška, V., Dlask, P.: Connections between organ pipe noise and Shannon entropy of the airflow: preliminary results. Acta Acustica United Acustica 103, 1100–1105 (2017)

    Article  Google Scholar 

  8. Humphrey, E.J., Bello, J.P., LeCun, Y.: Feature learning and deep architectures: new directions for music informatics. J. Intell. Inf. Syst. 41, 461–481 (2013). https://doi.org/10.1007/s10844-013-0248-5

    Article  Google Scholar 

  9. Koutini, K., Chowdhury, S., Haunschmid, V., Eghbal-zadeh H., Widmer, G.: Emotion and theme recognition in music with frequency-aware RF-regularized CNNs. MediaEval 1919, 27–29 October 2019. ArXiv abs/1911.05833. Sophia Antipolis (2019)

    Google Scholar 

  10. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012). https://doi.org/10.1145/3065386

  11. Lathi, B.P.: Linear systems and signals, 2nd edn. Oxford University Press, New York (2010)

    MATH  Google Scholar 

  12. Lehner, B., Widmer, G., Bock., S.: A low-latency, real-time-capable singing voice detection method with LSTM recurrent neural networks. In: 2015 23\(^{rd}\) European Signal Processing Conference (EUSIPCO), Nice, pp. 21 – 25 (2015). https://doi.org/10.1109/EUSIPCO.2015.7362337

  13. Rucz, P., Augusztinovicz, F., Angster, J., Preukschat, T., Miklos, A.: Acoustic behaviour of tuning slots of labial organ pipes.J. Acoust. Soc. Am. 5(135), 3056–3065 (2014). https://doi.org/10.1121/1.4869679

    Article  Google Scholar 

  14. Sakamoto, Y., Yoshikawa, S., Angster, J.: Acoustical investigations on the ears of flue or-GAN pipes. In: Forum Acusticum, pp. 647-651. EAA-Opakfi Hungary, Budapest (2005)

    Google Scholar 

  15. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014). https://doi.org/10.5555/2627435.2670313

    Article  MathSciNet  MATH  Google Scholar 

  16. Vaik, I., Paal, G.: Flow simulations on an organ pipe foot model. J. Acoust. Soc. Am. 2(133), 1102–1110 (2013). https://doi.org/10.1121/1.4773861

    Article  Google Scholar 

  17. Verge, M., Fabre, B., Mahu, W., Hirschberg, A., et al.: Jet formation and jet velocity fluctuations in a flue organ pipe. J. Acoust. Soc. Am. 2(95), 1119–1132 (1994). https://doi.org/10.1121/1.408460

    Article  Google Scholar 

  18. Widmer, G.: On the potential of machine learning for music research. In: Miranda, E.R. (ed.) Readings in Music and Artificial Intelligence. Routledge, New York (2013)

    Google Scholar 

  19. Zeiler, M.: ADADELTA: an adaptive learning rate method. https://arxiv.org/abs/1212.5701. Accessed 24 Apr 2020

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Acknowledgements

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. https://doi.org/10.1007/978-3-030-67148-8_7

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