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IEEE Transactions on Neural Systems and Rehabilitation Engineering | Vol.26, Issue.3 | | Pages 687-697
Perceptual and Model-Based Evaluation of Ideal Time-Frequency Noise Reduction in Hearing-Impaired Listeners
State-of-the-art hearing aids (HAs) try to overcome the deficit of poor speech intelligibility (SI) in noisy listening environments using digital noise reduction (NR) techniques. The application of time-frequency masks to the noisy sound input is a common NR technique to increase SI. The binary mask with its binary weights and the Wiener filter with continuous weights are representatives of a hard- and a soft-decision approach for time-frequency masking. In normal-hearing listeners, the ideal Wiener filter (IWF) outperforms the ideal binary mask (IBM) in terms of SI and speech quality with perfect SI even at very low signal-to-noise ratios. In this paper, both approaches were investigated for hearing-impaired (HI) listeners. Perceptual and auditory model-based measures were used for the evaluation. The IWF outperformed the IBM in terms of SI. Quality-wise, there was no overall difference between the NR algorithms perceived. Additionally, the processed signals were evaluated based on an auditory nerve model using the neurogram similarity metric (NSIM). The mean NSIM values were significantly different for intelligible and unintelligible sentences. The results suggest that a soft-mask seems to be promising for application in HAs.
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Perceptual and Model-Based Evaluation of Ideal Time-Frequency Noise Reduction in Hearing-Impaired Listeners
State-of-the-art hearing aids (HAs) try to overcome the deficit of poor speech intelligibility (SI) in noisy listening environments using digital noise reduction (NR) techniques. The application of time-frequency masks to the noisy sound input is a common NR technique to increase SI. The binary mask with its binary weights and the Wiener filter with continuous weights are representatives of a hard- and a soft-decision approach for time-frequency masking. In normal-hearing listeners, the ideal Wiener filter (IWF) outperforms the ideal binary mask (IBM) in terms of SI and speech quality with perfect SI even at very low signal-to-noise ratios. In this paper, both approaches were investigated for hearing-impaired (HI) listeners. Perceptual and auditory model-based measures were used for the evaluation. The IWF outperformed the IBM in terms of SI. Quality-wise, there was no overall difference between the NR algorithms perceived. Additionally, the processed signals were evaluated based on an auditory nerve model using the neurogram similarity metric (NSIM). The mean NSIM values were significantly different for intelligible and unintelligible sentences. The results suggest that a soft-mask seems to be promising for application in HAs.
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neurogram similarity metric nsim noisy sound input perceptual and auditory modelbased measures intelligible unintelligible softdecision approach timefrequency masking digital noise reduction nr algorithms signaltonoise approaches auditory nerve model continuous weights hard hearing aids normalhearing ideal wiener filter hearingimpaired hi listening speech intelligibility si softmask
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