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Automatic Singing Voice Recognition Employing Neural Networks and Rough Sets

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Transactions on Rough Sets IX

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 5390))

Abstract

The aim of the research study presented in this paper is the automatic recognition of a singing voice. For this purpose, a database containing sample recordings of trained and untrained singers was constructed. Based on these recordings, certain voice parameters were extracted. Two recognition categories were defined – one reflecting the skills of a singer (quality), and the other reflecting the type of the singing voice (type). The paper also presents the parameters designed especially for the analysis of a singing voice and gives their physical interpretation. Decision systems based on artificial neutral networks and rough sets are used for automatic voice quality/ type classification. Results obtained from both decision systems are then compared and conclusions are derived.

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References

  1. Bazan, J.G., Szczuka, M.S.: The Rough Set Exploration System. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  2. Bloothoof, G.: The sound level of the singers formant in professional singing. J. Acoust. Soc. Am. 79(6), 2028–2032 (1986)

    Article  Google Scholar 

  3. Childers, D.G., Skinner, D.P., Kemerait, R.C.: The Cepstrum: A Guide to Processing. Proc. IEEE 65, 1428–1443 (1977)

    Article  Google Scholar 

  4. Dejonckere, P.H., Olek, M.P.: Exactness of intervals in singing voice: A comparison between singing students and professional singers. In: Proc. 17th International Congress on Acoustics, Rome, VIII, pp. 120–121 (2001)

    Google Scholar 

  5. Diaz, J.A., Rothman, H.B.: Acoustic parameters for determining the differences between good and poor vibrato in singing. In: Proc. 17th International Congress on Acoustics, Rome, VIII, pp. 110–116 (2001)

    Google Scholar 

  6. Dziubiṅski, M., Kostek, B.: Octave Error Immune and Instantaneous Pitch Detection Algorithm. J. of New Music Research 34, 273–292 (2005)

    Article  Google Scholar 

  7. Fry, D.B.: Basis for the acoustical study of singing. J. Acoust. Soc. Am. 28, 789–798 (1957)

    Google Scholar 

  8. Harma, A.: Evaluation of a warped linear predictive coding scheme. In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 897–900 (2000)

    Google Scholar 

  9. Harma, A.: A comparison of warped and conventional linear predictive coding. IEEE Transactions on Speech and Audio Processing 5, 579–588 (2001)

    Article  Google Scholar 

  10. Herzel, H., Titze, I., Steinecke, I.: Nonlinear dynamics of the voice: signal analysis and biomechanical modeling. CHAOS 5, 30–34 (1995)

    Article  Google Scholar 

  11. Herrera, P., Serra, X., Peeters, G.: A proposal for the description of audio in the context of MPEG-7. In: Proc. CBMI European Workshop on Content-Based Multimedia Indexing, Toulouse, France (1999)

    Google Scholar 

  12. Joliveau, E., Smith, J., Wolfe, J.: Vocal tract resonances in singing: the soprano voice. J. Acoust. Soc. America 116, 2434–2439 (2004)

    Article  Google Scholar 

  13. Kostek, B.: Soft Computing in Acoustics, Applications of Neural Networks, Fuzzy Logic and Rough Sets to Music Acoustics, Studies in Fuzziness and Soft Computing. Physica Verlag, Heidelberg (1999)

    MATH  Google Scholar 

  14. Kostek, B., Czyżewski, A.: Representing Musical Instrument Sounds for Their Automatic Classification. J. Audio Eng. Soc. 49, 768–785 (2001)

    Google Scholar 

  15. Kostek, B.: Perception-Based Data Processing in Acoustics. In: Applications to Music Information Retrieval and Psychophysiology of Hearing. Series on Cognitive Technologies. Springer, Heidelberg (2005)

    Google Scholar 

  16. Kostek, B., Szczuko, P., Żwan, P., Dalka, P.: Processing of Musical Data Employing Rough Sets and Artificial Neural Networks. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 112–133. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  17. Kostek, B.: Applying computational intelligence to musical acoustics. Archives of Acoustics 32(3), 617–629 (2007)

    Google Scholar 

  18. Kruger, E., Strube, H.W.: Linear prediction on a warped frequency scale. IEEE Trans. on Acoustics, Speech, and Signal Processing 36(9), 1529–1531 (1988)

    Article  MATH  Google Scholar 

  19. Lindsay, A., Herre, J.: MPEG-7 and MPEG-7 Audio - An Overview. J. Audio Eng. Society 49(7/8), 589–594 (2001)

    Google Scholar 

  20. Mendes, A.: Acoustic effect of vocal training. In: Proc. 17th International Congress on Acoustics, Rome, VIII, pp. 106–107 (2001)

    Google Scholar 

  21. Pawlak, Z.: Rough Sets. International J. Computer and Information Sciences 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  22. Peters, J.F., Skowron, A. (eds.): Transactions on Rough Sets V. LNCS, vol. 4100. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  23. Rabiner, L.: On the use of autocorrelation analysis for pitch detection. IEEE Trans., ASSP 25, 24–33 (1977)

    Article  Google Scholar 

  24. Rough-set Exploration System, logic.mimuw.edu.pl/~rses/RSES_doc_eng.pdf

  25. Schutte, H.K., Miller, D.G.: Acoustic Details of Vibrato Cycle in Tenor High Notes. J. of Voice 5, 217–231 (1990)

    Article  Google Scholar 

  26. Sebestyen, G.S.: Decision-making processes in pattern recognition. Macmillan Publishing Co., Indianapolis (1965)

    Google Scholar 

  27. Sundberg, J.: The science of the singing voice. Northern Illinois University Press (1987)

    Google Scholar 

  28. Wieczorkowska, A., Czyżewski, A.: Rough Set Based Automatic Classification of Musical Instrument Sounds. Electr. Notes Theor. Comput. Sci. 82(4) (2003)

    Google Scholar 

  29. Wieczorkowska, A., Raṡ, Z.W.: Editorial: Music Information Retrieval. J. Intell. Inf. Syst. 21(1), 5–8 (2003)

    Article  Google Scholar 

  30. Wieczorkowska, A., Ras, Z.W., Zhang, X., Lewis, R.A.: Multi-way Hierarchic Classification of Musical Instrument Sounds, pp. 897–902. MUE, IEEE (2007)

    Google Scholar 

  31. Wolf, S.K.: Quantitative studies on the singing voice. J. Acoust. Soc. Am. 6, 255–266 (1935)

    Article  Google Scholar 

  32. Żwan, P.: Expert System for Automatic Classification and Quality Assessment of Singing Voices. 121 Audio Eng. Soc. Convention, San Francisco, USA (2006)

    Google Scholar 

  33. Żwan, P.: Expert system for objectivization of judgments of singing voices (in Polish), Ph.D. Thesis (supervisor: Kostek B.), Gdansk Univ. of Technology, Electronics, Telecommunications and Informatics Faculty, Multimedia Systems Department, Gdansk, Poland (2007)

    Google Scholar 

  34. Żwan, P., Kostek, B., Szczuko, P., Czyżewski, A.: Automatic Singing Voice Recognition Employing Neural Networks and Rough Sets. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 793–802. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  35. Żwan, P.: Automatic singing quality recognition employing artificial neural networks. Archives of Acoustics 33(1), 65–71 (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Multimedia Systems Department, Gdańsk University of Technology, Narutowicza 11/12, 80-952, Gdańsk, Poland

    Paweł Żwan, Piotr Szczuko, Bożena Kostek & Andrzej Czyżewski

Authors
  1. Paweł Żwan
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  2. Piotr Szczuko
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  3. Bożena Kostek
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  4. Andrzej Czyżewski
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  3. Institute of Computer Science, Warsaw University of Technology, Nowowoiejska 15/19, 00-665, Warsaw, Poland

    Henryk Rybiński

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Żwan, P., Szczuko, P., Kostek, B., Czyżewski, A. (2008). Automatic Singing Voice Recognition Employing Neural Networks and Rough Sets. In: Peters, J.F., Skowron, A., Rybiński, H. (eds) Transactions on Rough Sets IX. Lecture Notes in Computer Science, vol 5390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89876-4_25

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  • DOI: https://doi.org/10.1007/978-3-540-89876-4_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89875-7

  • Online ISBN: 978-3-540-89876-4

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