Skip to main content

A Real-time Volume Control System for Electric Guitars Based on Fuzzy Inference

  • Conference paper
  • First Online:
Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2022)

Abstract

Recently, musicians are writing songs at home instead of using the specialized recording equipment available in record studios. Also, high-quality recording requires that the volume should be close to the maximum level and the noise ratio be reduced when audio is input to the audio interface. In this paper, we propose a real-time volume control system for electric guitars based on fuzzy inference. Experimental results show that the proposed system can realize low volume and high-quality home recording by dynamically changing the volume of input audio based on fuzzy inference.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wahid, A.: Marketing communication adaptation in music industry in Indonesia amidst the Covid19 pandemic: a case study of independent musicians. KOMUNIKA 4(2), 137–149 (2021)

    Article  Google Scholar 

  2. Mehdi, S., et al.: Paradoxes of gender, technology, and the pandemic in the Iranian music industry. Popular Music Soc. 44(1), 1–13 (2021)

    Article  MathSciNet  Google Scholar 

  3. Lia, B., et. al.: Risk assessment of the spread of breathing air from wind instruments and singers during the COVID-19 pandemic. Weimar, Bauhaus-Universität Weimar, Chair of Building Physics (2020)

    Google Scholar 

  4. Hartmut, H.: Risk assessment of a coronavirus infection in the field of Music (2020)

    Google Scholar 

  5. Dylan, V., et. al.: COVID-19: impact on the musician and returning to singing; a literature review. J. Voice (2021)

    Google Scholar 

  6. Nagai, Y., et. al.: Approach of an emotion words analysis method related COVID-19 for twitter. In: 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE), pp. 1–2 (2021)

    Google Scholar 

  7. Nagai, Y., Saito, N., Hirata, A., Oda, T., Hirota, M., Katayama, K.: Approach of a Word2Vec based tourist spot collection method considering COVID-19. In: Barolli, L., Takizawa, M., Enokido, T., Chen, H.-C., Matsuo, K. (eds.) BWCCA 2020. LNNS, vol. 159, pp. 67–75. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-61108-8_7

    Chapter  Google Scholar 

  8. Nagai, Y., et. al.: Approach of a Japanese co-occurrence words collection method for construction of linked open data for COVID-19. In: 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), pp. 478–479 (2020)

    Google Scholar 

  9. Diana, T.: 2050 and beyond: a futurist perspective on musicians livelihoods. Music Educ. Res. 22(5), 596–610 (2020)

    Article  Google Scholar 

  10. Laurence, C.: 33 without music, life would be a mistake: the experience of a musician in Covid-19 times. In: Existentialism in Pandemic Times, pp. 35–44. Routledge (2022)

    Google Scholar 

  11. Riyan, H.: Music performance policy during covid-19 crisis: expectations versus reality. J. Adv. Soc. Sci. Policy, 1(1), 1–8 (2021)

    Google Scholar 

  12. Şebnem, A., et al.: Covid-19 Pandemisinde Müzisyen olmak. EJONS Int. J. Math. Eng. Natl. Sci. 5(17), 10–21 (2021)

    Google Scholar 

  13. Karen, N., et al.: COVID-19 and the creative music ecology. Crit. Stud. Improvisation/Études critiques en improvisation 14(1), 1–6 (2021)

    Google Scholar 

  14. Oliver, S.: COVID-19 puts musicians out of work. Green Left Wkly. 1258, 6 (2020)

    Google Scholar 

  15. Lee, D.A.: Impact of COVID-19 on virtual guitar communities. J. World Popular Music 9(1-2) 1–23 (2022)

    Google Scholar 

  16. Donovan. D.: USB Audio Interface: An Open-Source Reference Design for Digital Recording (2022)

    Google Scholar 

  17. Siddharth, K., et al.: USB capabilities and bootability of portable devices. Int. J. Sci. Eng. Res. 5, 496–500 (2014)

    Google Scholar 

  18. Yukawa, C., et. al.: Evaluation of a fuzzy-based robotic vision system for recognizing micro-roughness on arbitrary surfaces: a comparison study for vibration reduction of robot arm. In: International Conference on Network-Based Information Systems, pp. 230–237 (2022)

    Google Scholar 

  19. Saito, N., et. al.: Approach of fuzzy theory and hill climbing based recommender for schedule of life. In: Proceedings of IEEE LifeTech-2020, pp. 368–369 (2020)

    Google Scholar 

  20. Matsui, T., et. al.: FPGA implementation of a fuzzy inference based quadrotor attitude control system. In: Proceedings of IEEE GCCE-2021, pp. 691–692 (2021)

    Google Scholar 

  21. Yukawa, C., et. al.: Design of a fuzzy inference based robot vision for CNN training image acquisition. In: 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE), pp. 806–807 (2021)

    Google Scholar 

  22. Inaba, T., et al.: Performance evaluation of a QoS-aware fuzzy-based CAC for LAN access. Int. J. Space Based Situated Comput. 6(4), 228–238 (2016)

    Article  Google Scholar 

  23. Jarrah, A., et al.: Automotive volume control using fuzzy logic. J. Intell. Fuzzy Syst. 18(4), 329–343 (2007)

    MathSciNet  Google Scholar 

  24. Sanghoon, J., et. al.: A fuzzy inference-based music emotion recognition system. In: 2008 5th International Conference on Visual Information Engineering (VIE 2008), pp. 673–677 (2008)

    Google Scholar 

  25. Varun, O., et al.: Heuristic design of fuzzy inference systems: a review of three decades of research. Eng. Appl. Artif. Intell. 85, 845–864 (2019)

    Article  Google Scholar 

  26. Scott, H., et. al.: Profiling audio compressors with deep neural networks. In: Audio Engineering Society Convention, vol. 147 (2019)

    Google Scholar 

  27. Dimitrios, G., et al.: Digital dynamic range compressor design-a tutorial and analysis. J. Audio Eng. Soc. 60(6), 399–408 (2012)

    Google Scholar 

  28. Di, S.: Intelligent Control of Dynamic Range Compressor. Queen Mary University of London, Diss (2020)

    Google Scholar 

Download references

Acknowledgement

This work was supported by JSPS KAKENHI Grant Number JP20K19793.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tetsuya Oda .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Moriya, G. et al. (2023). A Real-time Volume Control System for Electric Guitars Based on Fuzzy Inference. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2022. Lecture Notes in Networks and Systems, vol 571. Springer, Cham. https://doi.org/10.1007/978-3-031-19945-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19945-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19944-8

  • Online ISBN: 978-3-031-19945-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics