SCH: INT: Collaborative Research: A Framework for Optimizing Hearing Aids In Situ Based on Patient Feedback, Auditory Context, and Audiologist Input

SCH：INT：协作研究：基于患者反馈、听觉环境和听力学家输入的现场优化助听器的框架

• 批准号: 1838897
• 负责人: Harinath Garudadri
• 金额: $ 46.74万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838897&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Framework

Twenty percent of Americans will be 65 years or older by 2030 out of which 35 - 50% will report having age-related hearing impairment that is treated primarily with hearing aids (HA). Regular use of HAs has been shown to improve communication and avoid the negative effects of hearing loss that include an increased risk of social isolation, depression, and inability to work, travel, or be physically active. Past research has shown that many people do not wear their HAs regularly, as they are unsatisfied with the performance in the real world. A fundamental limitation of existing methods for tuning HA is that they are not tailored to individual needs, which often leads to unsatisfactory performance. As part of this project, a team of computer scientists, engineers, and audiologists will develop new methods for tuning HAs that are based on the individual needs of a patient.The project is based on the approach that better HA configurations may be identified based on feedback from the patient that is collected in the moment and in situ. The project includes development of two systems for optimizing the configuration of HAs. The first system will improve the efficacy of the traditional tuning process by improving how feedback is obtained from patients, combined with auditory context information, and translated into HA configuration adjustments. The second system will automatically adapt the configuration of a HA based on patient feedback and auditory context information without requiring an audiologist to perform adjustments. The intellectual merit of this proposal includes the advances in machine learning necessary to model the performance of complex systems that have numerous parameters such as HAs and tuning their parameters based on feedback obtained from patients. Additionally, the project will advance the state-of-the-art in embedded systems by developing techniques to run HA optimization algorithms as part of a multi-tier system composed of HAs, mobile phones, and cloud services. It is anticipated that the proposed research will empower patients to become more involved in their 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.

到2030年，20%的美国人将达到65岁或以上，其中35%-50%的人将报告患有主要使用助听器(HA)治疗的年龄相关性听力障碍。经常使用HAS已被证明可以改善沟通，避免听力损失的负面影响，包括增加社交孤立、抑郁和无法工作、旅行或体力活动的风险。过去的研究表明，许多人并不经常戴假发，因为他们对自己在现实世界中的表现不满意。现有HA调优方法的一个基本限制是它们不能针对个人需求进行定制，这往往会导致性能不令人满意。作为该项目的一部分，一个由计算机科学家、工程师和听觉学家组成的团队将根据患者的个人需求开发调整HA的新方法。该项目基于一种方法，即可以根据从患者那里收集的即时和现场反馈来确定更好的HA配置。该项目包括开发两个系统，以优化HAS的配置。第一个系统将通过改进从患者那里获得反馈的方式，结合听觉环境信息，并将其转化为HA配置调整，来提高传统调整过程的效率。第二个系统将根据患者反馈和听觉环境信息自动调整HA的配置，而不需要听力专家进行调整。这一建议的智力价值包括在机器学习方面的进步，这是对具有许多参数(如HAS)的复杂系统的性能建模所必需的，并基于从患者获得的反馈来调整它们的参数。此外，该项目将通过开发技术来运行HA优化算法，作为由HAS、移动电话和云服务组成的多层系统的一部分，从而推动嵌入式系统中的最先进技术。预计拟议的研究将使患者能够更多地参与他们的听力护理，提高医管局的满意度，并丰富他们的社会互动。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

On Mitigating Acoustic Feedback in Hearing Aids with Frequency Warping by All-Pass Networks.

• DOI: 10.21437/interspeech.2019-3195
• 发表时间: 2019-09
• 期刊: Interspeech
• 作者: Lee CH;Chen KL;Harris F;Rao BD;Garudadri H
• 通讯作者: Garudadri H

Open speech platform: Web-apps for hearing aids research

开放语音平台：用于助听器研究的网络应用程序

• DOI: 10.1121/1.5137120
• 发表时间: 2019
• 期刊: The Journal of the Acoustical Society of America
• 作者: Zubatiy, Tamara;Bhattacharya, Uposhanto;Gan, Ziqi;Hamilton, Sean;Ganz, Chockalingam;Garudadri, Harinath
• 通讯作者: Garudadri, Harinath

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees.

ResNEsts 和 DenseNEsts：具有改进表示保证的基于块的 DNN 模型。

• 发表时间: 2021
• 期刊: Advances in neural information processing systems
• 作者: Chen,Kuan-Lin;Lee,Ching-Hua;Garudadri,Harinath;Rao,BhaskarD
• 通讯作者: Rao,BhaskarD

Self-fit generation of the wide range compression parameters in hearing aids

助听器中宽范围压缩参数的自拟合生成

• DOI: 10.1121/1.5137288
• 发表时间: 2019
• 期刊: The Journal of the Acoustical Society of America
• 作者: Bose, Apurba;Gan, Ziqi;Garudadri, Harinath
• 通讯作者: Garudadri, Harinath

Usability assessment of a wearable speech-processing platform

可穿戴语音处理平台的可用性评估

• DOI: 10.1121/1.5136985
• 发表时间: 2019
• 期刊: The Journal of the Acoustical Society of America
• 作者: Boothroyd, Arthur;Kirsch, Christine;Mackersie, Carol;Painter, Shaelyn;Garudadri, Harinath
• 通讯作者: Garudadri, Harinath