Investigating Differences in Preferred Noise Reduction Strength Among Hearing Aid Users

Abstract

Even though hearing aid (HA) users can respond very differently to noise reduction (NR) processing, knowledge about possible drivers of this variability (and thus ways of addressing it in HA fittings) is sparse. The current study investigated differences in preferred NR strength among HA users. Participants were groups of experienced users with clear preferences (“NR lovers”; N = 14) or dislikes (“NR haters”; N = 13) for strong NR processing, as determined in two earlier studies. Maximally acceptable background noise levels, detection thresholds for speech distortions caused by NR processing, and self-reported “sound personality” traits were considered as candidate measures for explaining group membership. Participants also adjusted the strength of the (binaural coherence-based) NR algorithm to their preferred level. Consistent with previous findings, NR lovers favored stronger processing than NR haters, although there also was some overlap. While maximally acceptable noise levels and detection thresholds for speech distortions tended to be higher for NR lovers than for NR haters, group differences were only marginally significant. No clear group differences were observed in the self-report data. Taken together, these results indicate that preferred NR strength is an individual trait that is fairly stable across time and that is not easily captured by psychoacoustic, audiological, or self-report measures aimed at indexing susceptibility to background noise and processing artifacts. To achieve more personalized NR processing, an effective approach may be to let HA users determine the optimal setting themselves during the fitting process.

Subjective evaluation of single microphone noise reduction with different time constants

“…For the participant in (A) the overall preference rating is stronger influenced by noise annoyance, while the overall preference rating of the participant in (B) is stronger influenced by speech naturalness. These different preference profiles have been reported previously in literature and can as yet not be related to any objective or subjective measure (Neher and Wagener 2016;Houben, Dijkstra, and Dreschler 2013;Brons, Houben, and Dreschler 2013). For participant (C) however, the ratings of all judgement criteria are rather alike.…”

“…Examples of such NR parameters are the type of noise estimator (Brons, Houben, and Dreschler 2012), the interaction with other HA signal processing such as compression (Kortlang et al 2018), or gain reduction strength. Regarding the latter, previous research on perceptual effects has shown a large variability of preferences for the maximum strength of gain reduction between listeners (Houben, Dijkstra, and Dreschler 2013;Neher and Wagener 2016). Due to noise estimation errors and processing artefacts, stronger noise reduction is accompanied with distortions that reduce speech quality (Bentler et al 2008).…”

Efficacy of a Hearing Aid Noise Reduction Function

“…The lack of significant findings could be due to measurement methods not being sensitive to changes and could also be due to differences in the NR algorithms used across studies ( Brons et al., 2015 ). In this study, sound quality was compared at positive S/N, which is probably best at revealing effects of NR function when speech intelligibility is already at a maximum ( Boymans & Dreschler, 2000 ; Brons et al., 2015 ; Neher, Grimm, Hohmann, & Kollmeier, 2014 ; Neher & Wagener, 2016 ). At low S/N, NR may not work as effectively in reducing noise annoyance ( Brons et al., 2013 ).…”

Hearing aid noise suppression and working memory function

“…For the bcf algorithm, we chose a gain function that resulted in a maximal attenuation of −20 dB for fully incoherent sounds (cf. Neher & Wagener, 2016, their Figure 1). To quantify ∆AI-SNR, we generated ~3 min of stimulus material per test condition and applied the gains calculated based on the signal mixture separately to the speech and noise signals (the so-called shadow-filtering method).…”