Synchronization and Self-Organization as Basis of Musical Performance, Sound Production, and Perception

  • Rolf Bader
Part of the Current Research in Systematic Musicology book series (CRSM, volume 1)


Nonlinearities and Self-organization are basic principles of many aspects of music production and perception, tone production of musical instruments, perception of timbre, movement, or tonality. This self-organizing nature is responsible for many musical instruments to play harmonic overtone series at all, leads to the tone development within the initial transient phase of tones, and is a basis for articulation and performance. In terms of movement, synergetic models are most suitable to explain sudden changes in performance and rhythmic patterns. Also with timbre, tone, and tonality perception, self-organizing models are very close to neural networks and so able to model perception as known from listeners accurately. Therefore, understanding musical performance needs close examination of the nonlinear and synergetic effects present in musical acoustics and music psychology. The paper also suggests an Impulse Pattern Formulation (IPF) as a basic production scheme of musical tones which comes very close to real musical instrument behavior, both in its steady-state as well as the transient phase of played notes.


Musical Instrument Sound Production Initial Transient Music Perception Organ Pipe 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Abel, M., Bergweiler, S., & Gerhard-Multhaupt, R. (2006). Synchronization of organ pipes: Experimental observations and modeling. The Journal of the Acoustical Society of America, 119, 2467–2475.CrossRefGoogle Scholar
  2. Arbi, M. A. (Ed.). (2003). The handbook of brain theory and neural networks (2nd ed.). Cambridge: MIT Press.Google Scholar
  3. Aschhoff, V. (1936). Experimentelle Untersuchungen an einer Klarinette. [Experimental investigations of a clarinet]. Akustische Zeitschrift, 1, 77–93.Google Scholar
  4. Bader, R. (2013). Musical acoustics and music psychology. In: Nonlinearities and synchronization. Heidelberg: Springer Series in Systematic Musicology (in print).Google Scholar
  5. Bader, R. (2010). Theoretical framework for initial transient and steady-state frequency amplitudes of musical instruments as coupled subsystems. In: Proceedings of 20th international symposium on music acoustics (ISMA) (pp. 1–8), Sydney and Katoomba.Google Scholar
  6. Bader, R., Diezt, M.-K., Elvers, P, Elias, M., & Tolkien, L. (2009). Foundation of a syllogistic music theory. In: Bader, R. (Ed.), Musical acoustics, neurocognition and psychology of music. Hamburger Jahrbuch fr Musikwissenschaft (Vol. 25 pp. 177–196).Google Scholar
  7. Bader, R., & Hansen, U. (2008). Acoustical analysis and modeling of musical instruments using modern signal processing methods. In D. Havelock, M. Vorlnder, & S. Kuwano (Eds.), Handbook of signal processing in acoustics (pp. 219–247). Berlin: Springer.Google Scholar
  8. Bader, R. (2008). Individual reed characteristics due to changed damping using coupled flow-structure and time-dependent geometry changing finite-element calculation. Proceedings Forum Acusticum joined with American Acoustical Society Paris, 08, 3405–3410.Google Scholar
  9. Bader, R. (2005). Computational mechanics of the classical Guitar. Berlin: Springer.Google Scholar
  10. Bader, R. (2005). Turbulent \({\text{k}}-\varepsilon\) model of flute-like musical instrument sound production. In: E. Lutton & J. Lévy-Véhel (Eds.), Fractals in Engineering. New trends in theory and applications (pp. 109–122). New York: Springer.Google Scholar
  11. Bader, R. & Markuse, B. (1994). Perception and analyzing methods of Groove in popular music. In: Systematische Musikwissenschaft II/1 (pp. 145–153).Google Scholar
  12. Bandyopadhyay, S., Shamma, S. A., & Kanold, P. O. (2010). Dichotomy of functional organization in the mouse auditory cortex. Nature Neuroscience, 13(3), 361–370.CrossRefGoogle Scholar
  13. Bank, B., & Sujbert, L. (2005). Generation of longitudinal vibrations in piano strings: From physics to sound synthesis. The Journal of the Acoustical Society of America, 117(4), 2268–2278.CrossRefGoogle Scholar
  14. Bender, D. (2011). Temporal processing in primate auditory cortex. Germany: Lambert Academic Publishing.Google Scholar
  15. Beurmann, A. & Schneider, A. (2009). Acoustics and sound of the harpsichord. Another case study. In: Bader, R. (Ed. / Hrsg.), Musical acoustics, neurocognition and psychology of music, current research at the Institute of Musicology, University of Hamburg. Hamburg Yearbook of Musicology (p. 25). Frankfurt: Peter Lang Verlag.Google Scholar
  16. Caclin, A., Smith, B. K., & Giard, M.-H. (2008). Interactive processing of timbre space dimensions: An exploration with event-related potentials. Journal of Cognitive Neuroscience, 20, 49–64.CrossRefGoogle Scholar
  17. Caclin, A., Brattico, E., Tervaniemi, M., Näänen, R., Morlet, D., Giard, M.-H., et al. (2006). Separate neural processing of timbre dimensions in auditory sensory memory. Journal of Cognitive Neuroscience, 18, 1959–1972.CrossRefGoogle Scholar
  18. Coltman, J. W. (1968). Sounding mechanism of the Flute and Organ Pipe. The Journal of the Acoustical Society of America, 44(4), 983–992.CrossRefGoogle Scholar
  19. Coltman, J. W. (1968). Acoustics of the Flute. In: Physics today (pp. 25–32).Google Scholar
  20. Cosi, P., De Poli, G., & Lauzzana, G. (1994). Auditory modelling and self-organizing neural networks for timbre classification. Journal of New Music Research, 23, 71–98.CrossRefGoogle Scholar
  21. Cremer, L., & Ising, H. (1967). Die selbsterregten Schwingungen von Orgelpfeifen. [The self-sustained vibrations of organ pipes.]. Acustica, 19, 143–153.Google Scholar
  22. Dalmont, J.-P., Gilbert, J., Kergomard, J., & Ollivier, S. (2005). An analytical prediction of the oscillation and extinction thresholds of a clarinet. The Journal of the Acoustical Society of America, 118(5), 3294–3305.CrossRefGoogle Scholar
  23. Damasio, A. R. (2006). Decartes’ error: Emotion. Vintage: Reason and the Human Brain.Google Scholar
  24. Daniel, P. (2008). Psychoacoustical roughness. In: D. Havelock, S. Kuwano, & Vorländer, M. (Eds.), Springer handbook of signal processing in acoustics (pp. 263–274). Berlin: Springer.Google Scholar
  25. Drennan, W. R., & Watson, Ch S. (2001). Sources of variation in profile analysis. II. Component spacing, dynamic changes, and roving level. Journal of the Acoustical Society of America, 110(5), 2498–2504.Google Scholar
  26. Duffour, P., & Woodhouse, J. (2004). Instability of systems with a frictional point contact: Part 1, basic Modelling. Journal of Sound and Vibration, 271, 365–390.CrossRefGoogle Scholar
  27. Duffour, P., & Woodhouse, J. (2004). Instability of systems with a frictional point contact: Part 2, model extensions J. of. Sound and Vibration, 271, 391–410.CrossRefGoogle Scholar
  28. Durbin, P. A., & Pettersson, R. (2001). Statistical theory and modeling for turbulent flows. New York: Wiley.zbMATHGoogle Scholar
  29. da Silva, A. R., Scavone, G. P., & van Salstijn, M. (2008). Numerical simulations of fluid-structure interactions in single-reed mouthpieces. The Journal of the Acoustical Society of America, 122(3), 1798–1809.CrossRefGoogle Scholar
  30. Dubnov, Sh, & Rodet, X. (2003). Investigation of phase coupling phenomena in sustained portion of musical instruments sound. The Journal of the Acoustical Society of America, 113(1), 348–359.CrossRefGoogle Scholar
  31. Elder, S. A. (1973). On the mechanism of sound production in organ pipes. The Journal of the Acoustical Society of America, 54(6), 1554–1564.CrossRefGoogle Scholar
  32. Fabre, B., Gilbert, J., Hirschberg, A., & Pelorson, X. (2012). Aeroacoustics of musical instruments. Annual Review of Fluid Mechanics, 44, 1–25.MathSciNetCrossRefGoogle Scholar
  33. Fabre, B., & Hirschberg, A. (2000). Physical modeling of flue instruments: A review of lumped models. Acta Acustica United with Acustica, 86, 599–610.Google Scholar
  34. Feiten, B., & Günzel, S. (1994). Automatic indexing of a sound database using self-organizing neural nets. Computer Music Journal, 18(3), 53–65.CrossRefGoogle Scholar
  35. Fletcher, N. H. (1978). Mode locking in nonlinearly excited inharmonic musical oscillators. The Journal of the Acoustical Society of America, 64, 1566–1569.CrossRefGoogle Scholar
  36. Florentine, M., Popper, A. N., & Fay, R. R. (2011). Loudness (p. 37). Berlin: Springer Handbook of Auditory Research.Google Scholar
  37. Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11, 127–138.CrossRefGoogle Scholar
  38. Garner, W. R. (1974). The processing of information and structure. New York: Wiley.Google Scholar
  39. Gjerdingen, R. O. (1990). Categorization of musical patterns by selforganizing neuronlike networks. Music Perception, 8, 339–370.CrossRefGoogle Scholar
  40. Green, D. M. (1988). Profile analysis: Auditory intensity discrimination. New York: Oxford University Press.Google Scholar
  41. Haken, H. (1990). Synergetics (3rd ed.). Berlin: Springer.Google Scholar
  42. Haken, H. (2002). Brain dynamics. Berlin: Springer.zbMATHGoogle Scholar
  43. Haken, H., Kelso, J. A. S., & Bunz, H. (1985). A theoretical model of phase transitions in human hand movements. Biological Cybernetics, 51, 347–356.MathSciNetzbMATHCrossRefGoogle Scholar
  44. Haken, H., Peper, C. E., Beek, P. J., & Daffertshofer, A. (1995). A model for phase transitions in human hand movements during multifrequency tapping. Physica D, 90, 179–196.CrossRefGoogle Scholar
  45. Haken, H., & Schiepek, G. (2006). Synergetik in der Psychologie [Synergetics in psychology]. Seattle: Hogrefe.Google Scholar
  46. von Helmholtz, H. (1863). Die Lehre von den Tonempfindungen als physiologische Grundlage fr die Theorie der Musik. [On the sensation of tone as a physiological basis for the theory of music]. Braunschweig: Vieweg.Google Scholar
  47. Heerra, P., Peeteres, G., & Dubnow, S. (2003). Automatic classification of musical instrument sounds. Journal of New Music Research, 32, 3–21.CrossRefGoogle Scholar
  48. Hirschberg, A., Gilbert, J., Msallam, R., & Wijnands, A. P. J. (1996). Shock waves in trombones. The Journal of the Acoustical Society of America, 99, 1754–1758.CrossRefGoogle Scholar
  49. Hofstadter, D. (1980). Gödel . Escher. Bach. An eternal golden braid: Vintage Books.Google Scholar
  50. Howe, M. S. (1975). Contributions to the theory of aerodynamic sound, with application to excell jet noise and the theory of the Flute. Journal of Fluid Mechanics, 71(4), 625–673.MathSciNetzbMATHCrossRefGoogle Scholar
  51. Lerdahl, F., & Jackendoff, R. (1983). Generative theory of tonal music. Cambridge: Cambridge University Press.Google Scholar
  52. Kaykayoglu, R., & Rockwell, D. (1986). Unstable jet-edge interaction. Part 1. Instantaneous pressure fields at a single frequency. Journal of Fluid Mechanics, 169, 125–149.CrossRefGoogle Scholar
  53. Kaykayoglu, R., & Rockwell, D. (1986). Unstable jet-edge interaction Part 2: Multiple frequency pressure fields. Journal of Fluid Mechanics, 169, 151–172.CrossRefGoogle Scholar
  54. Kepler, J., (Author) & Caspar, M., (1997). Weltharmonik [Harmonia Mundi, 1619]. München: R. Oldenbourg Verlag.Google Scholar
  55. Kepler, J., (Author), Duncan, A. M. (Transl.), Aiton, E. J. & Cohen, I. B., (Eds.). (1981). Mysterium cosmographicum. In: The secret of the universe [1596]. New York: Abaris Books.Google Scholar
  56. Keidel, W. D. (1975). Physiologie des Gehörs. [Physiology of the ear]. Stuttgart: Thieme.Google Scholar
  57. Klapuri, A. (2006). Signal processing methods for music transcription. Berlin: Springer.CrossRefGoogle Scholar
  58. Koelsch, S. (2012). Brain and music. New York: Wiley.Google Scholar
  59. Kolmogovov, A. N. (1941). The local structure of turbulence in incompressible viscous fluid for vary large Reynolds number. Dokl. Akad. Nauk SSSR, 30, 301–305.Google Scholar
  60. Kostek, B. (2005). Perception-based data processing in acoustics. In: Applications to music information retrieval and psychophysiology of hearing. Berlin: Springer.Google Scholar
  61. Krassnitzer, G. (2002). Multiphonics für Klarinette mit deutschem System und andere zeitgenössische Spielarten. [Multiphonics for clarinet with german system and other contemporary styles.] ed. ebenos Verlag Aachen.Google Scholar
  62. Krumhansl, C. L. (1990). Cognitive foundations of musical pitch. Oxford: Oxford University Press.Google Scholar
  63. Legge, K. A., & Fletcher, N. H. (1987). Non-linear mode coupling in symmetrically kinked bars. Jornal of Sound and Vibration, 118(1), 23–34.CrossRefGoogle Scholar
  64. Leman, M. & Carreras, F. (1997). Schema and gestalt: Testing the hypothesis of psychoneural isomorphism by computer simulation. In: M. Leman (Ed.), Music, gestalt, and computing. Studies in cognitive and systematic musicology (pp. 144–168). Berlin: Springer.Google Scholar
  65. Levitin, D. J. (2006). This is your brain on music. New York: Dutton.Google Scholar
  66. Lottermoser, W. (1983). Orgeln, Kirchen und Akustik. [Organs, churches, and acoustics]. Frankfurt: Verlag Erwin Bochinsky / Das Musikinstrument.Google Scholar
  67. Mamou-Mani, A., Frelat, J., & Besnainou, C. (2008). Numerical simulation of a piano soundboard under downbearing. The Journal of the Acoustical Society of America, 123, 2401–2406.CrossRefGoogle Scholar
  68. McIntyre, M. E., Schumacher, R. T., & Woodhouse, J. (1983). On the oscillations of musical instruments. The Journal of the Acoustical Society of America, 74(5), 1325–1345.CrossRefGoogle Scholar
  69. Nakanoy, M., Le Rouxz, J., Kameokaz, H., Kitanoy, Y., Onoy, N., & Sagayama, Sh. (2011). Nonnegative matrix factorization with Markov-chained bases for modeling time-varying patterns in music spectrograms. In: Proceedings of 2011 IEEE workshop on applications of signal processing to audio and acoustics (WASPAA2011) (pp. 325–328).Google Scholar
  70. Oertel, D. (Ed.). (2002). Integrated functions in the mammalian auditory pathway (p. 15). Berlin: Springer Handbook of Auditory Research.Google Scholar
  71. Platon, (Author), Cooper, J. M., & Hutchinson, D. S., (Eds). (1997). Platon. Complete works. Indianapolis: Hackett Publishing.Google Scholar
  72. Poeppel, D. (Ed.). (2012). The human auditory cortex (p. 43). Berlin: Springer Handbook of Auditory Research.Google Scholar
  73. Rao, R. P. N., Olshausen, B. A., & Lewicki, M. S. (2002). Probabilistic models of the brain. Perception and Neural Function: MIT Press.Google Scholar
  74. Rayleigh, Lord. J. W. S. (1945). The theory of sound (1894). New York: Reprint Dover.Google Scholar
  75. Repp, B. H. (2011). Comfortable synchronization of cyclic drawing movements with a metronome. Human Movement Science, 30, 18–39.CrossRefGoogle Scholar
  76. Repp, B. H. (2003). Rate limits in sensorimotor synchronization with auditory and visual sequences: The synchronization threshold and the benefits and costs of interval subdivision. Journal of Motor Behavior, 35, 355–370.CrossRefGoogle Scholar
  77. Reuter, Ch. (1995). Der Einschwingvorgan nichtperkussiver Musikinstrumente. [The initial transient of non-percussive musical instruments]. Bern: Peter Lang Verlag.Google Scholar
  78. Rossing, T. D., & Fletcher, N. H. (1983). Nonlinear vibrations in plates and gongs. The Journal of the Acoustical Society of America, 73, 345–351.CrossRefGoogle Scholar
  79. Ryugo, D. K., Fay, R. R., & Popper, A. N. (Eds.). (2011). Auditory and Vestibular Efferents (p. 38). Berlin: Springer Series of Auditory Research.Google Scholar
  80. Schneider, A., von Ruschkowski, A., & Bader, R. (2009) Klangliche Rauhigkeit, ihre Wahrnehmung und Messung. [Timbre rougness, its perception and measurement] In: Bader, R. (Ed.), Musical acoustics, neurocognition and psychology of music (Vol. 25 pp. 101–144). Germany: Hamburger Jahrbuch fr Musikwissenschaft.Google Scholar
  81. Seever, B. U. (2008). Masking and critical bands. In D. Havelock, S. Kuwano, & M. Vorländer (Eds.), Springer handbook of signal processing in acoustics (pp. 229–240). Berlin: Springer.Google Scholar
  82. Shamir, M., Ghitza, O., Epstein, S., & Kopell, N. (2009). Representation of time-varying stimuli by a network exhibiting oscillations on a faster time scale. PLoS Computational Biology, 5(5), e1000370.MathSciNetCrossRefGoogle Scholar
  83. Smith, E. C., & Lewicki, M. S. (2006). Efficient auditory coding. Nature, 439(23), 978–982.CrossRefGoogle Scholar
  84. Steinecke, I., & Herzel, H. (1995). Birurcations in an asymmetric vocal fold model. The Journal of the Acoustical Society of America, 97, 1874–1884.CrossRefGoogle Scholar
  85. Stevens, S. S. (1961). Procedure for calculation loudness: Mark VI. The Journal of the Acoustical Society of America, 33, 1577.CrossRefGoogle Scholar
  86. Stumpf, C. (1883/1890) Tonpsychologie. Bd.1/2.Google Scholar
  87. Thaut, M. (2005). Rhythm, music, and the brain. New York: Routledge.Google Scholar
  88. Todd, P., & Loy, G. (Eds.). (1991). Music and connectionism. Cambridge: MIT Press.Google Scholar
  89. Toiviainen, P. (1992). The organisation of timbres–A two-stage neural network model. In G. Widmer (Ed.), Proceedings of the ECAI-92 workshop on AI and Music. Vienna: Austrian Society for AI.Google Scholar
  90. Touzê, C., & Chaigne, A. (2000). Lyapunov exponents from experimental time series: Application to cymbal vibrations. Acta Acustica United with Acustica, 86, 557–567.Google Scholar
  91. Young, E. D., Yu, J. J., & Reiss, L. A. J. (2005). Non-linearities and the representation of auditory spectra. In M. S. Malmierca & D. R. F. Irvine (Eds.), Auditory spectral processing (pp. 136–168). Amsterdam: Elsevier Academic Press.Google Scholar
  92. Vaseghi, S. V. (2007). Multimedia signal processing: Theory and applications in speech, music, and communications. New York: Wiley.CrossRefGoogle Scholar
  93. Wang, X. (2010). Neurophysiological and computational principles of cortical rhythms in cognition. Physiological Reviews, 90(3), 1195–1268.CrossRefGoogle Scholar
  94. Woodhouse, J., & Schumacher, R. T. (1995). The transient behaviour of models of bowed-string motion. Chaos, 5, 509–523.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Institute of MusicologyUniversity of HamburgHamburgGermany

Personalised recommendations