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Modeling and Simulation of Hearing with Cochlear Implants: A Proposed Method for Better Auralization

  • A. M. KuczapskiEmail author
  • G.-D. Andreescu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 357)

Abstract

Cochlear implants are the most successful and widespread bionic prosthetics to restore hearing of deaf people by electrically stimulation of intra-cochlear nerve tissues. Several stimulation strategies were developed to convert sound in electric stimuli aiming to give better hearing quality. To help the development of new stimulation strategies, hearing simulations (auralization methods) were developed to synthesize perceived sound from electric stimuli. Existing auralization approaches are based on the observations that the stimulation place and rate of the cochlear nerve tissues generate perceived sounds of different frequencies and amplitudes. Although, auralization results can give some insight on the hearing quality, they completely ignore the adaptation capability of the auditory cortex and therefore it can represent only the perceived hearing of newly implanted patients. This paper presents fundaments of natural hearing and artificial hearing through cochlear implant, analyzes two main auralization methods, and finally proposes a novel auralization method. In the proposed method, the neural nerve firing pattern evoked by electric stimulation is fed to an artificial neural network trained to output the frequency domain representation of the original sound given by the electric stimuli. Then, the obtained frequency domain representation is transformed back to time domain. The main desired advantages of the novel auralization method are: (i) more accurate approximation of the perceived sound, (ii) possibility to differentiate between the hearing experience of experienced patients and newly implanted patients, and (iii) significant time reducing of research and development for new/improved stimulation strategies.

Keywords

Auditory models Auralization methods Cochlear implants Simulation Artificial neural networks 

References

  1. 1.
    Wilson BS, Dorman MF (2008) Cochlear implants: a remarkable past and a brilliant future. Hear Res 242(1–2):3–21CrossRefGoogle Scholar
  2. 2.
    Moctezuma A, Tu J (2011) An overview of cochlear implant systems. In: BIOE 414, University of Illinois, pp 1–20Google Scholar
  3. 3.
    Choi CTM, Lee Y-H (2012) A review of stimulating strategies for cochlear implants. In: Umat C (ed) Cochlear implant research updates. InTech, pp 77–89Google Scholar
  4. 4.
    Somek B, Fajt S, Dembitz A, Ivkovic M, Ostovic J (2006) Coding strategies for cochlear implants. Automatika 47(1–2):69–74Google Scholar
  5. 5.
    Schnupp J, Nelkel I, King A (2011) Auditory neuroscience: making sense of sound. MIT Press, CambridgeGoogle Scholar
  6. 6.
    Hochmair I, Nopp P, Jolly C, Schmidt M, Schößer H, Garnham C, Anderson I (2006) MED-EL cochlear implants: state of the art and a glimpse into the future. Trends Amplif 10(4):201–220CrossRefGoogle Scholar
  7. 7.
    Harczos T, Chilian A, Husar P (2013) Making use of auditory models for better mimicking of normal hearing processes with cochlear implants: the SAM coding strategy. IEEE Trans Biomed Circuits Syst 7(4):414–425CrossRefGoogle Scholar
  8. 8.
    Nogueira W, Buechner A (2012) Conveying low frequency information through analog electrical stimulation in cochlear implants. In: Proceedings of 20th European signal processing conference (EUSIPCO 2012), Bucharest, Romania, pp 509–513Google Scholar
  9. 9.
    Chen F, Zhang Y-t (2006) A new acoustic model incorporating temporal fine structure cue for cochlear implant. In: Proceedings of 5th International special topic conference on information technology in biomedicine (ITAB 200), Ioannina, Greece, pp 1–4Google Scholar
  10. 10.
    Mahalakshmi P, Reddy MR (2012) Investigation of the envelope and phase information for improved speech perception using an acoustic simulation model for cochlear implants. In: Proceedings of IEEE International EMBS conference on biomedical engineering and sciences (IECBES 2012), Langkawi, Malaysia, pp 555–558Google Scholar
  11. 11.
    Chilian A, Braun E, Harczos T (2011) Acoustic simulation of cochlear implant hearing. In: Proceedings of 3rd International symposium on auditory and audiological research (ISAAR 2011)—speech perception and auditory disorders, Nyborg, Denmark, pp 425–432Google Scholar
  12. 12.
    Loebach JL (2007) Cochlear implant simulations: a tutorial on generating acoustic simulations for research. In: Progress report no. 28 in research on spoken language processing, Indiana University, pp 359–368Google Scholar
  13. 13.
    Drennan WR, Rubinstein JT (2008) Music perception in cochlear implant users and its relationship with psychophysical capabilities. J Rehabil Res Dev 45(5):779–790CrossRefGoogle Scholar
  14. 14.
    Wang S, Xu L, Mannell R (2011) Relative contributions of temporal envelope and fine structure cues to lexical tone recognition in hearing-impaired listeners. J Assoc Res Otolaryngol 12(6):783–794CrossRefGoogle Scholar
  15. 15.
    Meddis R, Lopez-Poveda EA (2010) Auditory periphery: from pinna to auditory nerve. In: Meddis R et al (ed) Computational models of the auditory system. Springer, pp 7–38Google Scholar
  16. 16.
    Lopez-Poveda EA, Eustaquio-Martin A (2006) A biophysical model of the inner hair cell: the contribution of potassium currents to peripheral auditory compression. J Assoc Res Otolaryngol 7(3):218–235CrossRefGoogle Scholar
  17. 17.
    Tan Q, Carney LH (2003) A phenomenological model for the responses of auditory-nerve fibers: II. Nonlinear tuning with a frequency glide. J Acoust Soc Am 114(4):2007–2020CrossRefGoogle Scholar
  18. 18.
    Zhang X, Heinz MG, Bruce IC, Carney LH (2001) A phenomenological model for the responses of auditory-nerve fibers: I. Nonlinear tuning with compression and suppression. J Acoust Soc Am 109(2):648–670CrossRefGoogle Scholar
  19. 19.
    Harczos T, Szepannek G, Katai A, Klefenz F (2006) An auditory model based vowel classification. In: Proceedings of IEEE biomedical circuits and systems conference (BioCAS 2006), London, pp 69–72Google Scholar
  20. 20.
    Nogueira W, Harczos T, Edler B, Ostermann J, Buchner A (2007) Automatic speech recognition with a cochlear implant front-end. In: Proceedings of 8th annual conference of the international speech communication association (INTERSPEECH 2007), Antwerp, Belgium, pp 2537–2540Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Politehnica University of TimisoaraTimisoaraRomania

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