Microsystem Technologies

, Volume 24, Issue 5, pp 2429–2436 | Cite as

Guitar tuner using cepstral analysis and fuzzy controller on arduino board

  • Arvind Kumar
  • Sumit Srivastava
  • Mahesh Chandra
  • G. Sahoo
Technical Paper


Cepstral analysis and fuzzy controller is used to design an automatic guitar tuner on Arduino micro-controller. Signals from an acoustic guitar are fed into a system running MATLAB. Fundamental frequency of the played note is evaluated using cepstral analysis and is compared with desired set point. Frequency difference between the calculated frequency and the set frequency is used as an input to a fuzzy logic controller that generates a corresponding output as per the mentioned rules. This output from Fuzzy controller is used to generate a PWM signal with varying duty cycle. Output of the PWM signal is fed to a motor driver circuit which amplifies it and rotates the motor in appropriate direction with varying speed. This adjusts the tension in the string which results in change of frequency of the string to bring it to the desired pitch. System has been tested and verified for ‘A’ note and successful results were obtained with marginal offset. This paper highlights the use of cepstral analysis and arduino board for designing an automatic guitar tuner.


  1. Agostini G, Longari M, Pollastri E (2003) Musical instrument timbres classification with spectral features. EURASIP J Appl Signal Process 2003:5–14Google Scholar
  2. Barbancho AM, Klapuri A, Tardón LJ, Barbancho I (2012) Automatic transcription of guitar chords and fingering from audio. IEEE Trans Audio Speech Lang Process 20(3):915–921CrossRefGoogle Scholar
  3. Benward B (1981) Music in theory and practice. Instructor’s Manual, WC BrownGoogle Scholar
  4. Engineers Gallery (2017) Engineers gallery. Available at: (online)
  5. Fragoulis D, Papaodysseus C, Exarhos M, Roussopoulos G, Panagopoulos T, Kamarotos D (2006) Automated classification of piano-guitar notes. IEEE Trans Audio Speech Lang Process 14(3):1040–1050CrossRefGoogle Scholar
  6. Gupta A, Bowden R (2012) Fuzzy encoding for image classification using Gustafson–Kessel Algorithm. In: Image processing (ICIP), 2012 19th IEEE International Conference on 30 Sep 2012, pp 3137–3140Google Scholar
  7. Kaminsky I, Materka A (1995) Automatic source identification of monophonic musical instrument sounds. In: Neural Networks, Proceedings, IEEE International Conference on Nov 1995, vol 1, pp 189–194Google Scholar
  8. Klapuri A (2008) Multipitch analysis of polyphonic music and speech signals using an auditory model. IEEE Trans Audio Speech Lang Process 16(2):255–266CrossRefGoogle Scholar
  9. Mauch M, Dixon S (2010) Simultaneous estimation of chords and musical context from audio. IEEE Trans Audio Speech Lang Process 18(6):1280–1289CrossRefGoogle Scholar
  10. Perumal L, Nagi FH (2012) Switching Control system based on largest of maximum (LOM) defuzzification-theory and application. INTECH Open Access Publisher, LondonGoogle Scholar
  11. Rahnamai K, Cox B, Gorman K (2007) Fuzzy automatic guitar tuner. In: Fuzzy Information Processing Society, 2007. NAFIPS’07. Annual Meeting of the North American 24 Jun 2007, pp 195–199Google Scholar
  12. Ross TJ (2009) Fuzzy logic with engineering applications. Wiley, New YorkGoogle Scholar
  13. Stanojević MS, Bjelić MR (2011) Digital guitar tuner. In: Telecommunications Forum (TELFOR), 19th 2011 Nov 22, IEEE, pp 1574–1577Google Scholar
  14. Stevens SS, Volkmann J (1940) The relation of pitch to frequency: a revised scale. Am J Psychol 53(3):329–353CrossRefGoogle Scholar
  15. Villaran-Valdivia L (Inventor) (2014) Automatic guitar tuner. United States patent US 8,872,010. 2014, Oct 28Google Scholar
  16. Yazawa K, Itoyama K, Okuno HG (2014) Automatic transcription of guitar tablature from audio signals in accordance with player’s proficiency. In: Acoustics, speech and signal processing (ICASSP), 2014 IEEE International Conference on 4 May 2014, pp 3122–3126Google Scholar
  17. Zhang YG, Zhang CS (2006) Separation of music signals by harmonic structure modeling. In: Advances in neural information processing systems, pp 1617–1624Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of ECEBirla Institute of TechnologyMesraIndia
  2. 2.Department of Computer Science and EngineeringBirla Institute of TechnologyMesraIndia

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