This study investigated the effects of different adjustment criteria and sound scenes on self-adjusted hearing-aid gain settings. Self-adjusted settings were evaluated for speech recognition in noi...

This study investigated the adjustment behaviour of hearing aid (HA) users participating in a semi-supervised self-adjustment fine-tuning procedure for HAs. The aim was to link behaviour with the r...

This study presents a method of adding to clean speech signals a controlled degree of “musical” noise distortions that mimic typical artefacts of speech enhancement systems. The resulting distorted speech signals were evaluated with respect to listening effort and sound quality in subjective listening tests and via model predictions. Both subjective ratings and model prediction outcomes covered the entire rating scale from “excellent”/ “no effort” to “bad”/ “extreme effort”, respectively, in a consistent way. The proposed method proved to be useful for systematic assessments of “musical” noise distortions for the conditions tested in this study.

State-of-the-art stereo recording techniques using two microphones have two main disadvantages: first, a limited reduction of the reverberation in the direct sound component, and second, compression or expansion of the angular position of sound sources. To address these disadvantages, the aim of this study is the development of a true stereo recording microphone array that aims to record the direct and reverberant sound field separately. This array can be used within the recording and playback configuration developed in Grosse and van de Par, 2015. Instead of using only two microphones, the proposed method combines two logarithmically-spaced microphone arrays, whose directivity patterns are optimized with a superdirective beamforming algorithm. The optimization allows us to have a better control of the overall beam pattern and of interchannel level differences. A comparison between the newly-proposed system and existing microphone techniques shows a lower percentage of the recorded reverberance within the sound field.

This study investigated a previously evaluated self-adjustment procedure with respect to its applicability for asymmetrical hearing loss (AHL). Self-adjusted settings were evaluated for speech recognition in noise and sound preference. Participants were given the possibility to adjust the left and right hearing aid separately using a two-dimensional user interface. Two different adjustment sequences were tested. Realistic everyday sound scenes in a laboratory environment were presented. The difference between the ears regarding their speech recognition in noise was tested with two spatial conditions, unaided as well as with the prescriptive formula and the self-adjusted setting. Nineteen experienced hearing aid users (median age 76 years) with different degrees of AHL were invited to participate in this study. Participants adjusted a higher gain slope across frequency in the worse ear than in the better one. The two adjustment sequences resulted in significantly different adjustment durations and gain settings. The difference between the ears regarding speech recognition in noise did not change with the self-adjustment. Overall, group-mean effect sizes were small compared to the parameter space. The adjustment procedure can be used also by hearing aid users with AHL to find a possibly preferred gain setting.

Individuals have different preferences for setting hearing aid (HA) algorithms that reduce ambient noise but introduce signal distortions. “Noise haters” prefer greater noise reduction, even at the expense of signal quality. “Distortion haters” accept higher noise levels to avoid signal distortion. These preferences have so far been assumed to be stable over time, and individuals were classified on the basis of these stable, trait scores. However, the question remains as to how stable individual listening preferences are and whether day-to-day state-related variability needs to be considered as further criterion for classification. We designed a mobile task to measure noise-distortion preferences over 2 weeks in an ecological momentary assessment study with N = 185 (106 f, M _age = 63.1, SD _age = 6.5) individuals. Latent State-Trait Autoregressive (LST-AR) modeling was used to assess stability and dynamics of individual listening preferences for signals simulating the effects of noise reduction algorithms, presented in a web browser app. The analysis revealed a significant amount of state-related variance. The model has been extended to mixture LST-AR model for data-driven classification, taking into account state and trait components of listening preferences. In addition to successful identification of noise haters, distortion haters and a third intermediate class based on longitudinal, outside-of-the-lab data, we further differentiated individuals with different degrees of variability in listening preferences. Individualization of HA fitting could be improved by assessing individual preferences along the noise-distortion trade-off, and the day-to-day variability of these preferences needs to be taken into account for some individuals more than others.