通过模拟倾听选择的纵向动态来扩展噪声扭曲偏好的分类
Giulia Angonese1,2, Mareike Buhl1,3, Jonathan A Gößwein1,4
1Cluster of Excellence 'Hearing4all', Oldenburg, Germany.
Trends in hearing
|August 7, 2025
概括
对于降噪与信号扭曲的个人助听器偏好每天都会有所不同. 这项研究表明,考虑到这种变异性,可以改善助听器适合个性化的声音体验.
科学领域:
- 听力学 听力学是指听力学.
- 精神声学是一种精神声学.
- 人与计算机的交互
背景情况:
- 助听器 (HA) 算法平衡了降噪和信号扭曲,以满足不同的用户偏好.
- 现有的"讨厌噪音"和"讨厌扭曲"的分类假设稳定,长期的偏好.
- 这些倾听偏好的稳定性和日复一日的变化需要进一步研究以获得最佳的HA适配.
研究的目的:
- 调查个人倾听偏好的稳定性和动态性,以了解降噪/信号扭曲的权衡.
- 确定是否与日常状态相关的偏好变化应为助听器用户分类提供信息.
- 开发一个数据驱动的分类模型,包括稳定特征和动态状态偏好.
主要方法:
- 一项为期2周的生态瞬间评估研究,使用移动任务测量185名参与者的噪音扭曲偏好.
- 隐藏状态-特征自回归 (LST-AR) 建模用于分析倾听偏好的稳定性和动态.
- 扩展混合物LST-AR模型,用于基于状态和特征偏好组件的数据驱动分类.
主要成果:
- 发现了大量与国家相关的倾听偏好差异,表明了日复一日的波动.
- 一个混合LST-AR模型成功地将参与者分为"噪音讨厌者"",扭曲讨厌者"和中间类.
- 观察到倾听偏好变化的个体差异,这表明对偏好评估的需求有所不同.
结论:
- 对助听器算法的个人倾听偏好表现出明显的日常变化.
- 对助听器配件的个性化方法应该考虑稳定的偏好和它们的日常波动.
- 考虑偏好变化可以提高助听器设置的个性化,提高用户满意度.
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