SCH: Shallow and Deep Personalization for Hearing Aids

SCH：助听器的浅度和深度个性化

• 批准号: 2306331
• 负责人: Octav Chipara
• 金额: $ 119.91万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306331&HistoricalAwards=false
• 关键词: SCH; Shallow; Deep; Personalization; Hearing

Approximately 44.1 million adults in the US suffered from hearing loss, according to recent statistics. Untreated hearing impairment affects communication and can contribute to social isolation, depression, dementia, and reduced quality of life. The primary intervention for sensorineural hearing loss and related psychosocial consequences is hearing aid (HA) amplification. Unfortunately, only 15–30% of those who could benefit from HAs use them. A prerequisite for the successful adoption of HAs is effective signal processing algorithms coupled with personalization methods to configure their many parameters to improve speech understanding, sound quality, and users' subjective preferences. Therefore, this proposal focuses on developing new signal processing algorithms and configuration methods that empower people with hearing loss to meet their individualized hearing needs.This project aims to develop two approaches for personalizing HAs with different trade-offs in the degree of personalization, the amount of user effort required to find a satisfactory configuration, and their effectiveness in handling hearing losses of varying severity. It will develop shallow personalization techniques for configuring the parameters of existing HA signal-processing pipelines. These approaches provide more personalization options than state-of-the-art over-the-counter HAs by using different sub-band processing gains, compression parameters, and noise-reduction settings depending on the auditory context. These techniques are most suitable for patients with mild-to-moderate sensorineural hearing loss. We will also develop deep personalization techniques for training and personalizing HAs that use deep neural networks to amplify sounds. A unique aspect of this approach is using electroencephalogram signals combined with user feedback to drive the personalization process. These algorithms will benefit patients with more severe hearing loss or those in challenging auditory environments. The intellectual merit of this proposal is the new advancements in machine learning that are necessary to enable patients to configure and effectively use their HAs. The proposed research is anticipated to empower patients to become more involved in hearing care, improve HA satisfaction, and enrich their social interactions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

根据最近的统计数据，美国约有4410万成年人遭受听力损失。未经治疗的听力障碍会影响沟通，并可能有助于社会隔离，抑郁，痴呆和生活质量降低。感官听力损失和相关心理社会后果的主要干预措施是助听器（HA）扩增。不幸的是，只有15-30％可以从中受益的人使用它们。成功采用IS的先决条件是有效的信号处理算法以及个性化方法，以配置其许多参数，以提高语音理解，声音质量和用户的主题偏好。因此，该建议的重点是开发新的信号处理算法和配置方法，以赋予听力损失的人以满足其个性化的听力需求。该项目旨在开发两种个性化方法，具有不同的个性化程度，用户的努力，需要进行令人满意的配置及其在处理听力损失方面所需的用户努力。它将开发出浅的个性化技术，用于配置现有HA信号处理管道的参数。通过使用不同的子带处理收益，压缩参数和降噪设置，这些方法比最新的非处方药提供了更多的个性化选项，具体取决于听觉上下文。这些技术最适合轻度至中度感官听力损失的患者。我们还将开发深厚的个性化技术来进行培训和个性化，从而使用深层神经网络来放大声音。这种方法的一个独特方面是使用脑电图信号与用户反馈相结合来推动个性化过程。这些算法将使听力损失更严重或挑战听觉环境的患者受益。该提案的智力优点是机器学习的新进步，这是使患者能够配置和有效使用自己的拥有所必需的。拟议的研究预计将使患者有能力更多地参与听力护理，提高HA满足感并丰富他们的社交互动。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估来支持的。