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Computer-Based Analysis of Tomatis Listening Test System Audio Data

  • Félix Buendía-GarcíaEmail author
  • Manuel Agustí-Melchor
  • Cristina Pérez-Guillot
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 990)

Abstract

Audio information can be represented in a wide range of formats that need to be further processed. This is the case of data which are obtained from devices addressed to measure human listening levels. The current paper describes the computer-based analysis of different types of data related to the listening level measures coming from TLTS (Tomatis Listening Test System) devices. Such data can be classified into several formats such as images displaying listening graphical curves or spreadsheets collecting digitized values from those device measurements. In the first case, image analysis techniques were used to process listening curves gathered through the TLTS tests in the context of a collaboration project with the Isora Solutions company where the proposed system was applied. In the case of the spreadsheet data, a web-based tool was developed to complement the information processing of listening data sources which were gathered from the TLTS devices. The obtained results show the suitability of the implemented software tools to analyze different kind of information associated to the measurement of listening levels in the TLTS.

Keywords

Listening levels Image processing Web-based spreadsheet tools Tomatis Listening Test System 

Notes

Acknowledgements

Thanks to the support of the Research Project “Neurosensorial Stimulation for the Integration of English Language” Universitat Politècnica de València & Isora Solutions, 2016, the Computer Engineering department (DISCA) and the ETSINF (Escola Tècnica Superior d’Enginyeria Informàtica) at the Universitat Politècnica de València.

References

  1. 1.
    Mackersie, C.L., Boothroyd, A., Minniear, D.: Evaluation of the computer-assisted speech perception assessment test (CASPA). J. Am. Acad. Audiol. 12(8), 390–396 (2001)Google Scholar
  2. 2.
    Boothroyd, A.: CasperSent: an example of computer-assisted speech perception testing and training at the sentence level. J. Acad. Rehab. Audiol. 41, 30–50 (2006)Google Scholar
  3. 3.
    Eisenberg, L.S., Martinez, A.S., Boothroyd, A.: Assessing auditory capabilities in young children. Int. J. Pediatr. Otorhinolaryngol. 71, 1339–1350 (2007)CrossRefGoogle Scholar
  4. 4.
    Fernández, A., Ortega, M., Penedo, M.G.: computer aided hearing assessment: detection of eye gesture reactions as a response to the sound. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014. LNCS, vol. 8815, pp. 39–47. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11755-3_5CrossRefGoogle Scholar
  5. 5.
    Convery, E., Keidser, G., Seeto, M., Freeston, K., Zhou, D., Dillon, H.: Identification of conductive hearing loss using air conduction tests alone: reliability and validity of an automatic test battery. Ear Hear. 35(1), 1–8 (2014)CrossRefGoogle Scholar
  6. 6.
    Mahomed, F., Swanepoel, D.W., Eikelboom, R.H., Soer, S.: Validity of automated threshold audiometry: a systematic review and meta-analysis. Ear Hear. 34, 745–752 (2013)CrossRefGoogle Scholar
  7. 7.
    Perez, C., Garcia, C., Conejero, M., Capitán, A., Cerna, H.: Neurosensorial stimulation for the integration of English language. http://www.tomatis.com/es/el-metodo-tomatis/actualidad/neurosensorial-stimulation-for-the-integration-of-english-language-en.html. Last Accessed 31 Feb 2018
  8. 8.
    Mendel, L.L.: Objective and subjective hearing aid assessment outcomes. Am. J. Audiol. 16, 118–129 (2007)CrossRefGoogle Scholar
  9. 9.
    Tomatis, A.: The Ear and Language. Moulin Publishing, Norval (1996)Google Scholar
  10. 10.
    Broca, P.: Remarques sur le siege de la faculte du langage articule; suivies d’une observation d’aphemie. Bull. Soc. Anat. Paris 6, 398–407 (1861)Google Scholar
  11. 11.
    Wernicke, C.: The symptom of complex aphasia. In: Church, A.E. (ed.) Diseases of the nervous system, pp. 265–324. Appleton, New York (1911)Google Scholar
  12. 12.
    Deppe, M., et al.: Assessment of hemispheric language lateralization: a comparison between fMRI and fTCD. J. Cereb. Blood Flow Metab. 20(2), 263–268 (2000)CrossRefGoogle Scholar
  13. 13.
    Szaflarski, J.P., Holland, S.K., Schmithorst, V.J., Byars, A.W.: fMRI study of language lateralization in children and adults. Hum. Brain Mapp. 27(3), 202–212 (2006)CrossRefGoogle Scholar
  14. 14.
    Buendía-García, F., Agustí-Melchor, M., Pérez-Guillot, C., Cerna, H. Capitán, A.: A Computer-based framework to process audiometric signals using the tomatis listening test system. In: Proceedings of SIGMAP/International Joint Conference on e-Business and Telecommunications, pp. 25–34 Madrid, Spain (2017)Google Scholar
  15. 15.
    Fletcher, H.: Auditory patterns. Rev. Mod. Phys. 12(1), 47 (1940)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Félix Buendía-García
    • 1
    Email author
  • Manuel Agustí-Melchor
    • 1
  • Cristina Pérez-Guillot
    • 2
  1. 1.Computing Engineering DepartmentUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.Applied Linguistic DepartmentUniversitat Politècnica de ValènciaValenciaSpain

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