Research

• Efferent effects on human cochlear function.

• Differentiating inner from outer hair cell dysfunction in listeners with cochlear hearing loss.

• Simulating the response of the mammalian peripheral auditory system.

• Characterizing the response of the human basilar membrane in normal-hearing and hearing impaired listeners.

• The physiological mechanisms for encoding high-frequency spectral information at high levels.

• Speech processors for auditory prostheses, speech recognition and speaker identification systems.

Simulating the response of the mammalian peripheral auditory system

Computational models of the auditory-nerve response are useful tools for understanding the basic physiological processing underlying auditory perception.  Furthermore, the models may serve as an excellent start point from which to develop physiologically inspired speech processors for auditory prostheses (see below). Part of our work focuses on developing such computational models.

• Meddis, R, O'Mard, LPO, and Lopez-Poveda, EA.  (2001).  "A computational algorithm for computing non-linear auditory frequency selectivity," J. Acoust. Soc. Am. 109 (6): 2852-2861.

• Lopez-Poveda, EA, and Meddis, R. (2001).  "A human nonlinear cochlear filterbank," J. Acoust. Soc. Am. 110 (6): 3107-3118.

• Sumner, C, Lopez-Poveda, EA, O'Mard, LPO and Meddis, R.  (2002).  “A revised model of the inner hair cell and auditory nerve complex," J. Acoust. Soc. Am. 111 (5): 2178-2188.

• Lopez-Poveda, E. A., Eustaquio-Martín, A. (2006). "A biophysical model of the inner hair cell: The contribution of potassium current to peripheral compression," JARO-J. Assoc. Res. Otolaryngol. 7(3), 218-235.

Characterizing the response of the human basilar membrane

Applying our computational models to improve auditory prosthesis is possible only if the models reproduce the main characteristics of human auditory perception (e.g., frequency selectivity and compression). Part of our work is dedicated to measure these characteristics, both in normal-hearing listeners and in listeners with sensorineural hearing loss.

Selected publications:

• Lopez-Poveda, EA, Plack, CJ, and Meddis, R. (2003).  “Cochlear nonlinearity between 500 and 8000 Hz in normal-hearing listeners,” J. Acoust. Soc. Am. 113, 951-960.

• Plack, CJ, Drga, V, and Lopez-Poveda, EA (2004). "Inferred basilar-membrane response functions for listeners with mild to moderate sensorineural hearing loss," J. Acoust. Soc. Am. 115, 1684-1695.

• Lopez-Poveda, EA, Plack, CJ, Meddis, R, and Blanco, JL. (2005). "Cochlear compression between 500 and 8000 Hz in listeners with moderate sensorineural hearing loss," Hearing Res. 205, 172-183.

The physiological mechanisms for encoding high-frequency spectral information at high levels

High frequency spectral notches generated by the filtering action of the pinna are important cues for sound localization. Phase locking is absent in the response of auditory nerve fibers for frequencies above approximately 4 kHz. Therefore, high-frequency spectral notches must be encoded in the rate response of auditory nerve fibers. However, the rate-response representation of these notches degrades for high stimulus levels. This has been attributed to two factors: a) basilar-membrane filters get broader at high levels; and b) most nerve fibers have a narrow dynamic range.This yields some doubts as to whether spectral notches are perceived at high levels (e.g. Lopez-Poveda, 1996). The purpose of our work is to investigate the extent to which this true.

Selected publications:

• Lopez-Poveda, EA, and Meddis, R.  (1996).  "A physical model of sound diffraction and reflections in the human concha," J. Acoust. Soc. Am. 100, 3248-3259.

• Alves-Pinto, A., and Lopez-Poveda, E.A. (2005). "Detection of high-frequency spectral notches as a function of level," J. Acoust. Soc. Am. 118, 2458-2469.

• Alves-Pinto, A., Lopez-Poveda, E.A., and Palmer, A. R. (2005). "Auditory nerve encoding of high-frequency spectral information," in IWINAC 2005, J. Mira and J.R. Alvarez (Eds.), Lecture Notes in Computer Science 3561, 223-232.

• Lopez-Poveda EA, Alves-Pinto A, Palmer AR. (2007) "Psychophysical and physiological assessment of the representation of high-frecuency spectral notches in the auditory nerve," in Hearing: From Sensory Processing to Perception, edited by B Kollmeier, G Klump, V Hohmann, U Langemann, M Mauermann, S Uppenkamp, J Verhey.  Springer-Verlag, Heidelberg. pp. 51-59.

Speech processors

We design biologically-inspired speech processors based on our computational models of the human ear. We aim to apply these speech processors to improve cochlear-implant speech processors (in collaboration with Blake Wilson), speaker identification systems (in collaboration with the Crime Scene Investigation department of the Spanish Guardia Civil), and speech recognition systems.

Selected publications:

• Wilson BS, Schatzer R, Lopez-Poveda EA, Sun X, Lawson DT, Wolford RD. (2005). "Two new directions in speech processor design for cochlear implants,"  Ear & Hearing, 26, 73S-81S.

• Wilson BS, Schatzer R, Lopez-Poveda EA. (2006). "Possibilities for a closer mimicking of normal auditory functions with cochlear implants," in Cochlear Implants, 2nd Edition, edited by SB Waltzman and JT Roland, Thieme Medical Publishers, New York, pp. 48-56.

• Wilson BS, Lopez-Poveda EA, Schatzer R. (2010)."Use of auditory models in developing coding strategies for cochlear implants," in: Meddis, Lopez-Poveda, Popper, Fay (eds.) Computer Models of the Auditory System. Springer Handbook of Auditory Research, Springer, vol. 35, New York, chapter 9.