An Integrative Evaluation of the Efficacy of a Directional Microphone and Noise-Reduction Algorithm under Realistic Signal-to-Noise Ratios

Abstract

Background Many studies on the efficacy of directional microphones (DIRMs) and noise-reduction (NR) algorithms were not conducted under realistic signal-to-noise ratio (SNR) conditions. A Repeat-Recall Test (RRT) was developed previously to partially address this issue. Purpose This study evaluated whether the RRT could provide a more comprehensive understanding of the efficacy of a DIRM and NR algorithm under realistic SNRs. Possible interaction with listener working memory capacity (WMC) was assessed. Research Design This study uses a double-blind, within-subject repeated measures design. Study Sample Nineteen listeners with a moderate degree of hearing loss participated. Data Collection and Analysis The RRT was administered with participants wearing the study hearing aids (HAs) under two microphones (omnidirectional versus directional) by two NR (on versus off) conditions. Speech was presented from 0° at 75 dB SPL and a continuous noise from 180° at SNRs of 0, 5, 10, and 15 dB. The order of SNR and HA conditions was counterbalanced across listeners. Each test condition was completed twice in two 2-hour sessions separated by one month. Results The recall scores of listeners were used to group listeners into good and poor WMC groups. Analysis using linear mixed-effects models revealed significant effects of context, SNR, and microphone for all four measures (repeat, recall, listening effort, and tolerable time). NR was only significant on the listening effort scale in the DIRM mode at an SNR of 5 dB. Listeners with good WMC performed better on all measures of the RRT and benefitted more from context. Although DIRM benefitted listeners with good and poor WMC, the benefits differed by context and SNR. Conclusions The RRT confirmed the efficacy of DIRM and NR on several outcome measures under realistic SNRs. It also highlighted interactions between WMC and sentence context on feature efficacy.