Medizinische Physik and Cluster of Excellence "Hearing4all," Carl-von-Ossietzky Universität Oldenburg, Oldenburg, Germany.
Institut für Akustik, Technische Hochschule Lübeck, Lübeck, Germany.
Hear Res. 2022 Jul;420:108507. doi: 10.1016/j.heares.2022.108507. Epub 2022 Apr 11.
Spatial noise reduction algorithms ("beamformers") can considerably improve speech reception thresholds (SRTs) for bimodal cochlear implant (CI) users. The goal of this study was to model SRTs and SRT-benefit due to beamformers for bimodal CI users. Two existing model approaches varying in computational complexity and binaural processing assumption were compared: (i) the framework of auditory discrimination experiments (FADE) and (ii) the binaural speech intelligibility model (BSIM), both with CI and aided hearing-impaired front-ends. The exact same acoustic scenarios, and open-access beamformers as in the comparison clinical study Zedan et al. (2021) were used to quantify goodness of prediction. FADE was capable of modeling SRTs ab-initio, i.e., no calibration of the model was necessary to achieve high correlations and low root-mean square errors (RMSE) to both, measured SRTs (r = 0.85, RMSE = 2.8 dB) and to measured SRT-benefits (r = 0.96). BSIM achieved somewhat poorer predictions to both, measured SRTs (r = 0.78, RMSE = 6.7 dB) and to measured SRT-benefits (r = 0.91) and needs to be calibrated for matching average SRTs in one condition. Greatest deviations in predictions of BSIM were observed in diffuse multi-talker babble noise, which were not found with FADE. SRT-benefit predictions of both models were similar to instrumental signal-to-noise ratio (iSNR) improvements due to the beamformers. This indicates that FADE is preferrable for modeling absolute SRTs. However, for prediction of SRT-benefit due to spatial noise reduction algorithms in bimodal CI users, the average iSNR is a much simpler approach with similar performance.
空间降噪算法（“波束形成器”）可以显著提高双耳植入（CI）用户的言语接收阈值（SRT）。本研究的目的是为双耳 CI 用户建模由于波束形成器而导致的 SRT 和 SRT 收益。比较了两种现有的模型方法，它们在计算复杂性和双耳处理假设方面有所不同：（i）听觉辨别实验框架（FADE）和（ii）双耳语音可懂度模型（BSIM），都有 CI 和辅助听力受损的前端。使用完全相同的声学场景和与 Zedan 等人的比较临床研究（2021 年）相同的开放获取波束形成器来量化预测的准确性。FADE 能够从一开始就对 SRT 进行建模，即不需要对模型进行校准即可实现与测量 SRT（r = 0.85，RMSE = 2.8dB）和测量 SRT 收益（r = 0.96）的高度相关性和低均方根误差（RMSE）。BSIM 对测量 SRT（r = 0.78，RMSE = 6.7dB）和测量 SRT 收益（r = 0.91）的预测效果稍差，并且需要在一种条件下校准以匹配平均 SRT。BSIM 的预测中最大的偏差出现在扩散多说话者背景噪声中，而 FADE 中则没有观察到。两个模型的 SRT 收益预测都与由于波束形成器而导致的仪器信噪比（iSNR）改善相似。这表明 FADE 更适合于建模绝对 SRT。然而，对于双耳 CI 用户的空间噪声降低算法的 SRT 收益预测，平均 iSNR 是一种更简单的方法，具有相似的性能。
