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CHENFIT-AMP is a novel nonlinear strategy that combines the fitting (gain prescription) and amplification (gain implementation) procedures for cochlear hearing loss. The fitting part of CHENFIT-AMP prescribes gain for outer hair cell (OHC) and inner hair cell (IHC) loss, respectively. The gain for OHC loss varies with the cochlear gain decided by the value of OHC loss and the input level. The gain for IHC loss varies with the value of IHC loss only and will be limited to a constant if there is a “dead region.” The amplification part of CHENFIT-AMP is responsible for estimating the input level and cochlear gain based on Chen's loudness model. CHENFIT-AMP is evaluated with four typical audiograms and nine individual audiograms. A widely used nonlinear fitting procedure, NAL-NL2, is evaluated to compare prescription results with CHENFIT-AMP; a standard nonlinear amplification algorithm, multichannel compression (MCC), with the parameters provided by NAL-NL2, is also evaluated to compare amplification results with CHENFIT-AMP. For long-term average speech spectrum (LTASS) inputs, CHENFIT-AMP generally prescribes similar gain as NAL-NL2 for the typical audiograms; however, gain prescribed by CHENFIT-AMP is more individualized than NAL-NL2 for the individual audiograms, especially when the audiograms have big deviations in the slope. For LTASS-shaped noise input, the gain implemented by MCC with parameters provided by NAL-NL2 cannot completely realize the gain prescribed by NAL-NL2. For speech sentence inputs, average ratings by subjects indicated that amplification by CHENFIT-AMP was preferred and led to a louder perception than that by MCC with parameters from NAL-NL2. [ABSTRACT FROM PUBLISHER]

Abstract: A new method for calculating auditory excitation patterns and loudness for steady sounds is described. The method is based on a nonlinear filterbank in which each filter is the sum of a broad passive filter and a sharp active filter. All filters have a rounded-exponential shape. For each center frequency (CF), the gain of the active filter is controlled by the output of the passive filter. The parameters of the model were derived from large sets of previously published notched-noise masking data obtained from human subjects. Excitation patterns derived using the new filterbank include the effects of basilar membrane compression. Loudness can be calculated as the area under the excitation pattern when plotted in intensity-like units on an ERBN-number (Cam) scale; no transformation from excitation to specific loudness is required. The method predicts the standard equal-loudness contours and loudness as a function of bandwidth with good accuracy. With some additional assumptions, the method also gives reasonably accurate predictions of partial loudness. [Copyright &y& Elsevier]

Abstract: A model for calculating auditory excitation patterns and loudness for steady sounds for normal hearing is extended to deal with cochlear hearing loss. The filters used in the model have a double ROEX-shape, the gain of the narrow active filter being controlled by the output of the broad passive filter. It is assumed that the hearing loss at each audiometric frequency can be partitioned into a loss due to dysfunction of outer hair cells (OHCs) and a loss due to dysfunction of inner hair cells (IHCs). OHC loss is modeled by decreasing the maximum gain of the active filter, which results in increased absolute threshold, reduced compressive nonlinearity and reduced frequency selectivity. IHC loss is modeled by a level-dependent attenuation of excitation level, which results in elevated absolute threshold. The magnitude of OHC loss and IHC loss can be derived from measures of loudness recruitment and the measured absolute threshold, using an iterative procedure. The model accurately fits loudness recruitment data obtained using subjects with unilateral or highly asymmetric cochlear hearing loss who were required to make loudness matches between tones presented alternately to the two ears. With the same parameters, the model predicted loudness matches between narrowband and broadband sound reasonably well, reflecting loudness summation. The model can also predict when a dead region is present. [Copyright &y& Elsevier]