Department of Speech, Language and Hearing Sciences, Indiana University Bloomington.
Department of Speech and Hearing Sciences, University of Washington, Seattle.
Am J Audiol. 2024 Sep 3;33(3):793-809. doi: 10.1044/2024_AJA-24-00011. Epub 2024 Jun 14.
Most modern hearing aids (HAs) employ wide dynamic range compression (WDRC) and noise reduction (NR) algorithms. It is known that the nonlinear effects of WDRC and NR cause changes to the output signal-to-noise ratio (SNR) of an HA. However, the relative contributions of WDRC and NR to the nonlinear effects are not fully understood. The current study investigated (a) whether WDRC or NR dominates the nonlinear effects measured at the output of a digital HA and (b) whether the electroacoustic effectiveness of NR depends on WDRC parameters while input SNR and background noise are systematically varied.
Test stimuli were Connected Speech Test sentences in multitalker babble noise (2- or 20-talker), presented at input SNRs ranging from -10 to +10 dB. The HA was programmed using multiband WDRC set according to the National Acoustic Laboratories for Nonlinear HA fitting formula 2 prescriptive fits for four standard audiograms and two compression speeds. The NR algorithm of the HA was switched on or off in separate conditions. Nonlinear electroacoustic effects from the WDRC and NR algorithms were assessed by measuring the output SNR of the HA using a phase-inversion technique. To investigate whether there are other factors that may be important besides the output SNR, the Hearing Aid Speech Intelligibility Index and the Hearing Aid Speech Quality Index were applied to the recordings to generate inferences on aided speech intelligibility and perceived speech quality.
Results showed that WDRC dominated the net nonlinear effect at low-input SNRs, and the net nonlinear effect of WDRC and NR was reduced at high-input SNRs. Results also showed that the effectiveness of NR depended on compression parameters. The effectiveness of NR was partially explained by the trend of Hearing Aid Speech Intelligibility Index and Hearing Aid Speech Quality Index scores, potentially indicating that the Hearing Aid Speech Intelligibility Index and Hearing Aid Speech Quality Index scores may capture factors that cannot be captured by the output SNR metric.
Results suggest that the individual signal-processing stages in an HA should not be considered as independent. Electroacoustic evaluation of WDRC and NR algorithms in isolation is not sufficient to capture the combined nonlinear effect of the two algorithms.
大多数现代助听器（HA）采用宽动态范围压缩（WDRC）和降噪（NR）算法。已知 WDRC 和 NR 的非线性效应会导致 HA 输出信号噪声比（SNR）发生变化。然而，WDRC 和 NR 对非线性效应的相对贡献尚不完全清楚。本研究调查了（a）WDRC 或 NR 是否主导数字 HA 输出端测量的非线性效应，以及（b）当输入 SNR 和背景噪声系统变化时，NR 的电声有效性是否取决于 WDRC 参数。
测试刺激为多说话者嘈杂环境中的连接语音测试句子（2 或 20 个说话者），输入 SNR 范围从-10 到+10 dB。根据国家声学实验室非线性 HA 拟合公式 2 对四个标准听力图和两种压缩速度的多频带 WDRC 进行 HA 编程。在单独的条件下打开或关闭 HA 的 NR 算法。使用相位反转技术测量 HA 的输出 SNR，评估 WDRC 和 NR 算法的非线性电声效应。为了研究除输出 SNR 之外是否还有其他重要因素，应用助听器语音可懂度指数和助听器语音质量指数对录音进行分析，以得出辅助语音可懂度和感知语音质量的推论。
结果表明，在低输入 SNR 时，WDRC 主导净非线性效应，而在高输入 SNR 时，WDRC 和 NR 的净非线性效应降低。结果还表明，NR 的有效性取决于压缩参数。NR 的有效性部分可以由助听器语音可懂度指数和助听器语音质量指数评分的趋势来解释，这可能表明助听器语音可懂度指数和助听器语音质量指数评分可能捕捉到无法通过输出 SNR 指标捕捉到的因素。
结果表明，HA 中的各个信号处理阶段不应被视为独立的。单独对 WDRC 和 NR 算法进行电声评估不足以捕获这两种算法的综合非线性效应。
