Page 37 - 2017Spring

Biosketch
Fletcher, H., and Galt, R. (1950). Perception of speech and its relation to telephony. The Journal of the Acoustical Society of America 22, 89-151.
French, N. R., and Steinberg, J. C. (1947). Factors governing the intelligibility of speech sounds. The Journal of the Acoustical Society of America 19, 90-119.
Hines, A., and Harte, N. (2012). Speech intelligibility prediction using a Neurogram Similarity Index Measure. Speech Communication 54, 306- 320. doi:10.1016/j.specom.2011.09.004.
Hornsby, B. W. Y. (2004). The Speech Intelligibility Index: What is it and what’s it good for? The Hearing Journal 57, 10-17.
Hossain, M. E., Jassim, W. A., and Zilany, M. S. A. (2016). Reference-free assessment of speech intelligibility using bispectrum of an auditory neu- rogram. PLoS ONE 11, e0150415. doi:10.1371/journal.pone.0150415.
Jørgensen, S., and Dau, T. (2011). Predicting speech intelligibility based on the signal-to-noise envelope power ratio after modulation-frequency se- lective processing. The Journal of the Acoustical Society of America 130, 1475-1487. doi:10.1121/1.3621502.
Kates, J. M., and Arehart, K. H. (2014). The Hearing-Aid Speech Percep- tion Index (HASPI). Speech Communication 65, 75-93. doi:10.1016/j.spe- com.2014.06.002.
Liberman, M. C. (2016). Noise-induced hearing loss: Permanent versus tempo- rary threshold shifts and the effects of hair cell versus neuronal degeneration. In Popper, A. N., and Hawkins, A. (Eds.), The Effects of Noise on Aquatic Life II, Springer-Verlag, New York, pp. 1-7. doi:10.1007/978-1-4939-2981-8_1.
Lopez-Poveda, E. A., and Barrios, P. (2013). Perception of stochastically un- dersampled sound waveforms: A model of auditory deafferentation. Fron- tiers in Neuroscience 7, 1-13. doi:10.3389/fnins.2013.00124.
Mamun, N., Jassim, W. A., and Zilany, M. S. A. (2015). Prediction of speech intelligibility using a neurogram orthogonal polynomial measure (NOPM). IEEE/ACM Transactions on Audio, Speech, and Language Pro- cessing 23, 760-773. doi:10.1109/TASLP.2015.2401513.
Rallapalli, V. H., and Heinz, M. G. (2016). Neural spike-train analyses of the speech-based envelope power spectrum model: Application to predicting individual differences with sensorineural hearing loss. Trends in Hearing 20, 1-14. doi:10.1177/2331216516667319.
Rhebergen, K. S., Versfeld, N. J., and Dreschler, W. A. (2006). Extended speech intelligibility index for the prediction of the speech reception threshold in fluctuating noise. The Journal of the Acoustical Society of America 120, 3988-3997. doi:10.1121/1.2358008.
Sachs, M. B., Bruce, I. C., Miller, R. L., and Young, E. D. (2002). Biological basis of hearing aid design. Annals of Biomedical Engineering 30, 157-168. doi:10.1114/1.1458592.
Steeneken, H. J. M., and Houtgast, T. (1980). A physical method for mea- suring speech-transmission quality. The Journal of the Acoustical Society of America 67, 318-326.
Swaminathan, J., and Heinz, M. G. (2012). Psychophysiological analyses demonstrate the importance of neural envelope coding for speech percep- tion in noise. Journal of Neuroscience 32, 1747-1756. doi:10.1523/JNEU- ROSCI.4493-11.2012.
Zilany, M. S. A., and Bruce, I. C. (2006). Modeling auditory-nerve responses for high sound pressure levels in the normal and impaired auditory pe- riphery. The Journal of the Acoustical Society of America 120, 1446-1466. doi:10.1121/1.2225512.
Zilany, M. S. A., and Bruce, I. C. (2007). Predictions of speech intelligibility with a model of the normal and impaired auditory-periphery. Proceedings of 3rd International IEEE/EMBS Conference on Neural Engineering, Kohala Coast, HI, May 2-5, 2007, pp. 481-485. doi:10.1109/cne.2007.369714.
Zilany, M. S. A., Bruce, I. C., and Carney, L. H. (2014). Updated param- eters and expanded simulation options for a model of the auditory pe- riphery. The Journal of the Acoustical Society of America 135, 283-286. doi:10.1121/1.4837815.
Ian C. Bruce completed his bachelor’s degree in electrical and electronic engi- neering at the University of Melbourne, Melbourne, VIC, Australia, and his PhD at the university’s Bionic Ear Insti- tute. In between, he was a research and teaching assistant at the University of Technology in Vienna, Austria. He did
a postdoctoral fellowship in the Department of Biomedical Engineering at the Johns Hopkins University in Baltimore, MD, before joining the faculty at McMaster University in Hamilton, ON, Canada, in 2002. He is currently an associ- ate professor in electrical and computer engineering and is engaged in interdisciplinary activities in neuroscience, bio- medical engineering, psychology, and music cognition.
References
Allen, J. B. (1996). Harvey Fletcher’s role in the creation of communication acoustics. The Journal of the Acoustical Society of America 99, 1825-1839.
American National Standards Institute (ANSI). (1997). ANSI S3.5-1997. Methods for Calculation of the Speech Intelligibility Index. American Na- tional Standards Institute, New York.
Bondy, J., Bruce, I. C., Becker, S., and Haykin, S. (2004). Predicting speech intelligibility from a population of neurons. In Thrun, S., Saul, L., and Schölkopf, B. (Eds.), Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, pp. 1409-1416.
Brownell, W. E. (1997). How the ear works - Nature’s solutions for listening. Volta Review 99, 9-28.
Bruce, I. C., and Zilany, M. S. A. (2007). Modelling the effects of cochlear impairment on the neural representation of speech in the auditory nerve and primary auditory cortex. In Dau, T., Buchholz, J., Harte, J. M., and Christiansen, T. U. (Eds.), Auditory Signal Processing in Hearing-Impaired Listeners, International Symposium on Audiological and Auditory Research (ISAAR), Danavox Jubilee Foundation, Nyborg, Denmark, August 23-25, 2007, pp. 1-10.
Bruce, I. C., Léger, A. C., Moore, B. C. J., and Lorenzi, C. (2013). Physi- ological prediction of masking release for normal-hearing and hear- ing-impaired listeners. Proceedings of Meetings on Acoustics: ICA 2013 Montreal, Montreal, QC, Canada, June 2-7, 2013, vol. 19, 050178. doi:10.1121/1.4799733.
Chabot-Leclerc, A., Jørgensen, S., and Dau, T. (2014). The role of auditory spectro-temporal modulation filtering and the decision metric for speech intelligibility prediction. The Journal of the Acoustical Society of America 135, 3502-3512. doi:10.1121/1.4873517.
Chittka, L., and Brockmann, A. (2005). Perception space—The final fron- tier. PLoS Biology 3, e137. doi:10.1371/journal.pbio.0030137.
Christiansen, C., Pedersen, M. S., and Dau, T. (2010). Prediction of speech intelligibility based on an auditory preprocessing model. Speech Commu- nication 52, 678-692. doi:10.1016/j.specom.2010.03.004.
Elhilali, M., Chi, T., and Shamma, S. A. (2003). A spectro-temporal modu- lation index (STMI) for assessment of speech intelligibility. Speech Com- munication 41, 331-348. doi:10.1016/S0167-6393(02)00134-6.
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