面向精准听力学的稳健数据驱动听觉分析。
Robust Data-Driven Auditory Profiling Towards Precision Audiology.
机构信息
Hearing Systems Section, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark.
出版信息
Trends Hear. 2020 Jan-Dec;24:2331216520973539. doi: 10.1177/2331216520973539.
The sources and consequences of a sensorineural hearing loss are diverse. While several approaches have aimed at disentangling the physiological and perceptual consequences of different etiologies, hearing deficit characterization and rehabilitation have been dominated by the results from pure-tone audiometry. Here, we present a novel approach based on data-driven profiling of perceptual auditory deficits that attempts to represent auditory phenomena that are usually hidden by, or entangled with, audibility loss. We hypothesize that the hearing deficits of a given listener, both at hearing threshold and at suprathreshold sound levels, result from two independent types of "auditory distortions." In this two-dimensional space, four distinct "auditory profiles" can be identified. To test this hypothesis, we gathered a data set consisting of a heterogeneous group of listeners that were evaluated using measures of speech intelligibility, loudness perception, binaural processing abilities, and spectrotemporal resolution. The subsequent analysis revealed that distortion type-I was associated with elevated hearing thresholds at high frequencies and reduced temporal masking release and was significantly correlated with elevated speech reception thresholds in noise. Distortion type-II was associated with low-frequency hearing loss and abnormally steep loudness functions. The auditory profiles represent four robust subpopulations of hearing-impaired listeners that exhibit different degrees of perceptual distortions. The four auditory profiles may provide a valuable basis for improved hearing rehabilitation, for example, through profile-based hearing-aid fitting.
感音神经性听力损失的来源和后果多种多样。虽然有几种方法旨在厘清不同病因的生理和感知后果,但听力缺陷的特征描述和康复一直受到纯音听力测试结果的主导。在这里,我们提出了一种基于感知听觉缺陷数据驱动分析的新方法,试图描述通常被可听度损失掩盖或混淆的听觉现象。我们假设,给定听众的听力缺陷,无论是在听力阈值还是在阈上声音水平,都源于两种独立类型的“听觉失真”。在这个二维空间中,可以识别出四个不同的“听觉特征”。为了验证这一假设,我们收集了一组由具有不同听力水平的听众组成的数据,这些听众使用言语可懂度、响度感知、双耳处理能力和频谱时间分辨率等测量方法进行了评估。随后的分析表明,失真类型 I 与高频听力阈值升高和时间掩蔽释放减少有关,与噪声中言语接受阈值升高显著相关。失真类型 II 与低频听力损失和异常陡峭的响度函数有关。听觉特征代表了具有不同程度感知失真的四种听力受损听众的稳健亚群。这四种听觉特征可能为改善听力康复提供有价值的基础,例如,通过基于特征的助听器适配。