Skip to main content
Log in

A model of perceptual segregation based on clustering the time series of the simulated auditory nerve firing probability

  • Original Paper
  • Published:
Biological Cybernetics Aims and scope Submit manuscript

Abstract

This paper introduces a model that accounts quantitatively for a phenomenon of perceptual segregation, the simultaneous perception of more than one pitch in a single complex sound. The method is based on a characterization of the time-varying spike probability generated by a model of cochlear responses to sounds. It demonstrates how the autocorrelation theories of pitch perception contain the necessary elements to define a specific measure in the phase space of the simulated auditory nerve probability of firing time series. This measure was motivated in the first instance by the correlation dimension of the attractor; however, it has been modified in several ways in order to increase the neurobiological plausibility. This quantity characterizes each of the cochlear frequency channels and gives rise to a channel clustering criterion. The model computes the clusters and the pitch estimates simultaneously using the same processing mechanisms of delay lines; therefore, it respects the biological constraints in a similar way to temporal theories of pitch. The model successfully explains a wide range of perceptual experiments.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Balaguer-Ballester E, Denham S (2006) Unified temporal method for perceptual fusion and pitch perception. In: Fifteenth annual computational neuroscience meeting CNS 31–32

  • Balaguer-Ballester E, Palomares A, Martin JD and Soria E (2006). Predicting service request in support centres based on nonlinear dynamics, ARMA models and neural networks. Expert Syst Appl 10: 1016–1025

    Google Scholar 

  • Bernstein JGW and Oxenham AJ (2005). An autocorrelation model with place dependence to account for the effect of harmonic number on fundamental frequency discrimination. J Acoust Soc Am 117: 3816–3831

    Article  PubMed  Google Scholar 

  • Brunstrom JM and Roberts B (1998). Profiling the perceptual suppression of partials in periodic complex tones:Further evidence for harmonic template. J Acoust Soc Am 104: 3511–3519

    Article  PubMed  CAS  Google Scholar 

  • Brunstrom JM and Roberts B (2000). Separate mechanisms govern the selection of the spectral components for perceptual fusion and for the computation of global pitch. J Acoust Soc Am 107: 1566–1577

    Article  PubMed  CAS  Google Scholar 

  • Culling JF and Darwin CJ (1993). Perceptual separation of simultaneous vowel: within and across-formant grouping by F0. J Acoust Soc Am 93: 3454–3467

    Article  PubMed  CAS  Google Scholar 

  • Dayan P and Abbot LF (2001). Theoretical neuroscience. Cambridge University press, Cambridge, USA

    Google Scholar 

  • Cheveigné A (2005). Pitch perception models. In: Plack, CJ, Oxenham, AJ, Fay, RR and Popper, AN (eds) Pitch: neural coding and perception, Springer, New York

    Google Scholar 

  • Cheveigné A and Kawahara H (2002). YIN, a fundamental frequency estimator for speech and music. J Acoust Soc Am 111: 1917–1930

    Article  PubMed  Google Scholar 

  • Cheveigné A and Pressnitzer D (2006). The case of the missing delay lines: Synthetic delays obtained by cross-channel phase interaction. J Acoust Soc Am 119: 3908–3918

    Article  PubMed  Google Scholar 

  • Cheveigné A (1998). Cancellation model of pitch perception. J Acoust Soc Am 103: 1261–1271

    Article  PubMed  Google Scholar 

  • Denham SL (2005). Dynamic Iterated Ripple Noise: further evidence for the importance of temporal processing in auditory perception. Biosystems 79: 199–206

    Article  PubMed  Google Scholar 

  • Goldstein JL (1973). An optimum processor theory for the central information of the pitch of complex tones. J Acoust Soc Am 54: 1496–1516

    Article  PubMed  CAS  Google Scholar 

  • Grose JH, Hall JW and Buss E (2002). Virtual pitch integration for asynchronous harmonics. J Acoust Soc Am 112: 2956–2961

    Article  PubMed  Google Scholar 

  • Hall JW and Peters RW (1981). Pitch for nonsimultaneous succesive harmonics in quiet and noise. J Acoust Soc Am 69: 509–513

    Article  PubMed  Google Scholar 

  • Hancock KE, Davis KA and Voltg HF (1997). Modelling inhibition of type II units in the dorsal cochlear nucleus. Biol Cybern 76: 417–428

    Article  Google Scholar 

  • Hartman WM (1996). Pitch periodicity and auditory organization. J Acoust Soc Am 100: 3491–3502

    Article  Google Scholar 

  • Kantz H and Schreiber T (1999). Nonlinear time series analysis. Cambridge University press, Cambridge USA

    Google Scholar 

  • Krumbholz K, Patterson RD, Seither-Preisler A, Lammertmann C and Lutenhoner L (2003). Neuromagnetic evidence for a pitch processing centre in Heschl’s Gyrus. Cerebral Cortex 13: 765–772

    Article  PubMed  CAS  Google Scholar 

  • Li JY and Hartman WM (1998). The pitch of a mistuned harmonic: evidence for a template model. J Acoust Soc Am 103: 2608–2617

    Article  Google Scholar 

  • Licklider J (1951). A duplex theory of pitch perception. Experientia 7: 128–134

    Article  PubMed  CAS  Google Scholar 

  • Licklider J (1959). Three auditory theories. In: Koch, S (eds) Psychology a study of a science, pp 41–144. McGraw-Hill, New York

    Google Scholar 

  • Lopez-Poveda EA and Meddis R (2001). A human nonlinear cochlear filter bank. J Acoust Soc Am 110: 3170–3118

    Article  Google Scholar 

  • Lyon RH (1984). Range and frequency dependence of transfer function phase. J Acoust Soc Am 76: 1433–1439

    Article  Google Scholar 

  • Meddis R and Hewitt MJ (1991). Virtual pitch and phase sensitivity of a computer model of the auditory periphery: I. Pitch identification. J Acoust Soc Am 89: 2866–2882

    Article  Google Scholar 

  • Meddis R and Hewitt MJ (1991). Virtual pitch and phase sensitivity of a computer model of the auditory periphery: II. Phase sensitivity. J Acoust Soc Am 89: 2883–2894

    Article  Google Scholar 

  • Meddis R and Hewitt MJ (1992). Modelling the identification of concurrent vowels with different fundamental frequencies. J Acoust Soc Am 91: 233–245

    Article  PubMed  CAS  Google Scholar 

  • Meddis R and O’Mard L (1997). A unitary model of pitch perception. J Acoust Soc Am 102: 1811–1820

    Article  PubMed  CAS  Google Scholar 

  • Meddis R and O’Mard L (2006). Virtual pitch in a computational physiological model. J Acoust Soc Am 120: 3861–3868

    Article  PubMed  Google Scholar 

  • Moore BCJ and Glasberg BR (1983). Suggested formulae for calculating auditory-filter bandwidths and excitation patterns. J Acoust Soc Am 74: 750–753

    Article  PubMed  CAS  Google Scholar 

  • Plack CJ and White LJ (2000). Perceived continuity and pitch perception. J Acoust Soc Am 108: 1162–1169

    Article  PubMed  CAS  Google Scholar 

  • Provenzale A, Smith LA, Vio R and Murante G (1992). Distinguishing between low-dimensional dynamics and randomness in measured time series. Physica D 5: 28–31

    Google Scholar 

  • Roberts B (2005). Spectral pattern Grouping and the pitches of complex tones and their components. Acta Acustica United Acustica 91: 945–957

    Google Scholar 

  • Roberts B and Bailey PJ (1996). Spectral regularity as a factor distinct from harmonic relations in auditory grouping. J Exp Physchol Hum Percept Perform 22: 604–614

    Article  CAS  Google Scholar 

  • Roberts B and Brunstrom JM (2001). Perceptual fusion and fragmentation of complex tones made inharmonic by applying different degrees of frequency shift and spectral stretch. J Acoust Soc Am 110: 2479–2490

    Article  PubMed  CAS  Google Scholar 

  • Roberts B and Holmes SD (2006). Grouping and the pitch of a mistuned fundamental component: Effects of applying simultaneous multiple mistunings to the other harmonics. Hear Res 222: 79–88

    Article  PubMed  Google Scholar 

  • Sauer T, Yorke JA and Casdagli M (1991). Embedology. J Stat Phys 65: 579–590

    Article  Google Scholar 

  • Sumner CJ, Lopez-Poveda EA, O’Mard LP and Meddis R (2003). Adaptation in a revised inner-hair cell model. J Acoust Soc Am 113: 893–901

    Article  PubMed  Google Scholar 

  • Takens F (1981). Detecting strange attractors in turbulence. Springer Lecture Notes in Mathematics vol. 898. Springer, New York

    Google Scholar 

  • Theiler J (1988). Lacunarity in a best estimator of fractal dimension. Phys Lett A 135: 195–210

    Article  Google Scholar 

  • Wiegrebe L (2001). Searching for the time constant in of neural pitch extraction. J Acoust Soc Am 107: 1082–1091

    Article  Google Scholar 

  • Winter IM (2005). The neurophysiology of pitch. In: Plack, CJ, Oxenham, AJ, Fay, RR and Popper, AN (eds) Pitch: neural coding and perception., Springer, New York

    Google Scholar 

  • Yost WA (1996). Pitch and pitch strength of iterated rippled noise: Is it the envelope or fine structure?. J Acoust Soc Am 100: 2720–2730

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Emili Balaguer-Ballester.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Balaguer-Ballester, E., Coath, M. & Denham, S.L. A model of perceptual segregation based on clustering the time series of the simulated auditory nerve firing probability. Biol Cybern 97, 479–491 (2007). https://doi.org/10.1007/s00422-007-0187-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00422-007-0187-8

Keywords

Navigation