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An Auditory-Masking-Threshold-Based Noise Suppression Algorithm GMMSE-AMT[ERB] for Listeners with Sensorineural Hearing Loss

Abstract

This study describes a new noise suppression scheme for hearing aid applications based on the auditory masking threshold (AMT) in conjunction with a modified generalized minimum mean square error estimator (GMMSE) for individual subjects with hearing loss. The representation of cochlear frequency resolution is achieved in terms of auditory filter equivalent rectangular bandwidths (ERBs). Estimation of AMT and spreading functions for masking are implemented in two ways: with normal auditory thresholds and normal auditory filter bandwidths (GMMSE-AMT[ERB]-NH) and with elevated thresholds and broader auditory filters characteristic of cochlear hearing loss (GMMSE-AMT[ERB]-HI). Evaluation is performed using speech corpora with objective quality measures (segmental SNR, Itakura-Saito), along with formal listener evaluations of speech quality rating and intelligibility. While no measurable changes in intelligibility occurred, evaluations showed quality improvement with both algorithm implementations. However, the customized formulation based on individual hearing losses was similar in performance to the formulation based on the normal auditory system.

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Correspondence to Ajay Natarajan.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Natarajan, A., Hansen, J.H., Arehart, K.H. et al. An Auditory-Masking-Threshold-Based Noise Suppression Algorithm GMMSE-AMT[ERB] for Listeners with Sensorineural Hearing Loss. EURASIP J. Adv. Signal Process. 2005, 678405 (2005). https://doi.org/10.1155/ASP.2005.2938

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Keywords and phrases

  • normal hearing
  • hearing impaired
  • auditory masking threshold
  • equivalent rectangular bandwidth
  • generalized minimum mean square estimation