通过模拟聆听选择的纵向动力学来扩展噪声失真偏好的分类。

IF 3 2区 医学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY
Trends in Hearing Pub Date : 2025-01-01 Epub Date: 2025-08-07 DOI:10.1177/23312165251362018
Giulia Angonese, Mareike Buhl, Jonathan A Gößwein, Birger Kollmeier, Andrea Hildebrandt
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引用次数: 0

摘要

个人对设置助听器(HA)算法有不同的偏好,这些算法可以减少环境噪声,但会引入信号失真。“讨厌噪音的人”喜欢更大幅度的降噪,甚至不惜牺牲信号质量。“防失真”接受更高的噪声水平,以避免信号失真。到目前为止,这些偏好被认为随着时间的推移是稳定的,个体是根据这些稳定的特征得分进行分类的。然而,问题仍然是个人的听力偏好有多稳定,以及是否需要将日常状态相关的变化作为进一步的分类标准。在一项生态瞬时评估研究中,我们设计了一个移动任务,在2周内测量噪声失真偏好,N = 185 (106 f, Mage = 63.1, SDage = 6.5)个个体。使用潜在状态-特质自回归(LST-AR)模型来评估个人对模拟网络浏览器应用程序中降噪算法效果的信号的收听偏好的稳定性和动态。分析显示了大量与状态相关的方差。该模型被扩展为混合LST-AR模型,用于数据驱动分类,考虑了听力偏好的状态和特征成分。除了根据实验室外的纵向数据成功识别噪音厌恶者、失真厌恶者和第三个中间类别外,我们还进一步区分了听力偏好差异程度不同的个体。通过评估个人对噪声失真权衡的偏好,可以改善HA拟合的个性化,并且需要对这些偏好的日常变化进行更多的考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Toward an Extended Classification of Noise-Distortion Preferences by Modeling Longitudinal Dynamics of Listening Choices.

Toward an Extended Classification of Noise-Distortion Preferences by Modeling Longitudinal Dynamics of Listening Choices.

Toward an Extended Classification of Noise-Distortion Preferences by Modeling Longitudinal Dynamics of Listening Choices.

Toward an Extended Classification of Noise-Distortion Preferences by Modeling Longitudinal Dynamics of Listening Choices.

Individuals have different preferences for setting hearing aid (HA) algorithms that reduce ambient noise but introduce signal distortions. "Noise haters" prefer greater noise reduction, even at the expense of signal quality. "Distortion haters" accept higher noise levels to avoid signal distortion. These preferences have so far been assumed to be stable over time, and individuals were classified on the basis of these stable, trait scores. However, the question remains as to how stable individual listening preferences are and whether day-to-day state-related variability needs to be considered as further criterion for classification. We designed a mobile task to measure noise-distortion preferences over 2 weeks in an ecological momentary assessment study with N = 185 (106 f, Mage = 63.1, SDage = 6.5) individuals. Latent State-Trait Autoregressive (LST-AR) modeling was used to assess stability and dynamics of individual listening preferences for signals simulating the effects of noise reduction algorithms, presented in a web browser app. The analysis revealed a significant amount of state-related variance. The model has been extended to mixture LST-AR model for data-driven classification, taking into account state and trait components of listening preferences. In addition to successful identification of noise haters, distortion haters and a third intermediate class based on longitudinal, outside-of-the-lab data, we further differentiated individuals with different degrees of variability in listening preferences. Individualization of HA fitting could be improved by assessing individual preferences along the noise-distortion trade-off, and the day-to-day variability of these preferences needs to be taken into account for some individuals more than others.

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来源期刊
Trends in Hearing
Trends in Hearing AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGYOTORH-OTORHINOLARYNGOLOGY
CiteScore
4.50
自引率
11.10%
发文量
44
审稿时长
12 weeks
期刊介绍: Trends in Hearing is an open access journal completely dedicated to publishing original research and reviews focusing on human hearing, hearing loss, hearing aids, auditory implants, and aural rehabilitation. Under its former name, Trends in Amplification, the journal established itself as a forum for concise explorations of all areas of translational hearing research by leaders in the field. Trends in Hearing has now expanded its focus to include original research articles, with the goal of becoming the premier venue for research related to human hearing and hearing loss.
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