Andrew T Sabin1, Lauren Hardies, Nicole Marrone, Sumitrajit Dhar. 1. Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois 60640, USA. a-sabin@northwestern.edu
Abstract
OBJECTIVES: The frequency-gain curve (FGC) is among the most important parameters to consider when fitting a hearing aid. In practice, a prescriptive FGC, derived from the audiogram, is initially applied. In the subsequent fine-tuning stage, the patient often communicates their concerns about the sound quality using descriptors (e.g., "it sounds hollow") and the clinician modifies the FGC accordingly. In this study, we present and evaluate a method that could enhance this process by rapidly mapping descriptors to FGC shapes. In addition, we begin to use this method to examine the extent to which there is across-individual agreement in how descriptors map to FGC shapes. DESIGN: Ten listeners with hearing loss rated the extent to which each of a series of FGCs captured the meaning of a particular descriptor. Regression analyses were conducted to determine the degree to which these ratings were correlated with the gain values associated with each of 25 frequency bands. The array of slopes of these regression lines across frequency bands is termed the weighting function and was interpreted as the FGC shape that corresponded to the descriptor. We used this procedure to determine the FGC shapes associated with four of the most common descriptors used to describe hearing aid sound quality problems ("tinny," "sharp," "hollow," and "in a barrel, tunnel, or well"). RESULTS: The weighting function shape was highly replicable despite variable listener responses, reached asymptotic performance quickly (<20 ratings), and was predictive of listener responses. On the global level, there was some agreement across individuals about how common descriptors mapped to weighting function shape. However, considerable differences were apparent between individuals in terms of the specifics of that mapping. CONCLUSIONS: The current approach for descriptor-to-FGC mapping is a quick, reliable method for determining individualized changes to the FGC. Given the range of individual differences in the specifics of the descriptor-to-FGC mappings observed, this approach could be useful in a clinical setting to easily quantify these acoustic parameters. Implementation of such procedures could lead to more personalized fine-tuning of amplification devices.
OBJECTIVES: The frequency-gain curve (FGC) is among the most important parameters to consider when fitting a hearing aid. In practice, a prescriptive FGC, derived from the audiogram, is initially applied. In the subsequent fine-tuning stage, the patient often communicates their concerns about the sound quality using descriptors (e.g., "it sounds hollow") and the clinician modifies the FGC accordingly. In this study, we present and evaluate a method that could enhance this process by rapidly mapping descriptors to FGC shapes. In addition, we begin to use this method to examine the extent to which there is across-individual agreement in how descriptors map to FGC shapes. DESIGN: Ten listeners with hearing loss rated the extent to which each of a series of FGCs captured the meaning of a particular descriptor. Regression analyses were conducted to determine the degree to which these ratings were correlated with the gain values associated with each of 25 frequency bands. The array of slopes of these regression lines across frequency bands is termed the weighting function and was interpreted as the FGC shape that corresponded to the descriptor. We used this procedure to determine the FGC shapes associated with four of the most common descriptors used to describe hearing aid sound quality problems ("tinny," "sharp," "hollow," and "in a barrel, tunnel, or well"). RESULTS: The weighting function shape was highly replicable despite variable listener responses, reached asymptotic performance quickly (<20 ratings), and was predictive of listener responses. On the global level, there was some agreement across individuals about how common descriptors mapped to weighting function shape. However, considerable differences were apparent between individuals in terms of the specifics of that mapping. CONCLUSIONS: The current approach for descriptor-to-FGC mapping is a quick, reliable method for determining individualized changes to the FGC. Given the range of individual differences in the specifics of the descriptor-to-FGC mappings observed, this approach could be useful in a clinical setting to easily quantify these acoustic parameters. Implementation of such procedures could lead to more personalized fine-tuning of amplification devices.