Using Machine Learning to Mitigate Reverberation Effects in Cochlear Implants

使用机器学习减轻人工耳蜗的混响效应

• 批准号: 9100672
• 负责人: LESLIE M. COLLINS
• 金额: $ 29.39万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9100672
• 关键词: Acoustics; Address; Affect; Algorithms; Categories; Cochlear Implants; Comprehension; Development; Devices; Ensure; Environment; Environmental Risk Factor; Frequencies; Health; Hearing; Hearing Impaired Persons; Home environment; Individual; Location; Machine Learning; Maps; Masks; Methods; Modeling; National Institute on Deafness and Other Communication Disorders; Noise; Performance; Problem Solving; Process; Quality of life; Recruitment Activity; Research; Signal Transduction; Source; Speech; Speech Perception; Stimulus; Surface; Testing; Time; base; improved; response; simulation; sound; speech processing; speech recognition; success

 DESCRIPTION (provided by applicant): Cochlear implants (CIs) provide hearing for over 200,000 recipients worldwide {NIDCD, 2011 #637}. These devices successfully provide high levels of speech understanding in quiet listening conditions; however, more challenging conditions degrade speech comprehension for CI recipients to a much greater degree than for normal hearing listeners {Kokkinakis, 2011 #673;Nelson, 2003 #302}. CI listeners are especially affected by reverberant conditions with even a small level of reverberation degrading comprehension to a greater degree than a large amount of steady-state noise {Hazrati, 2012 #674}. Thus, a method that mitigates the effects of reverberation has the potential to greatly improve the quality of life for CI users. Previous attempts to solve the problem of speech in reverberation for cochlear implants have not been able to be implemented in real time. Our preliminary results suggest that successful mitigation of overlap masking can result in a substantial improvement in speech recognition even if self-masking is not mitigated and we have devised an approach that can be implemented in real time. In the proposed effort, we will first improve the classifier to detect reverberation based on our successful preliminary efforts. Next we will assess the mitigation algorithm, first in normal hearing listeners and then in listeners with cochlear implants. Finally, we will implement the algorithm in real time and again test it.

 描述（由申请人提供）：耳蜗植入体（CI）为全球超过200，000名接受者提供听力{NIDCD，2011 #637}。这些设备在安静的收听条件下成功地提供了高水平的语音理解;然而，更具挑战性的条件使CI接受者的语音理解比正常听力收听者的语音理解降级更大的程度{Kokkinakis，2011 #673;纳尔逊，2003 #302}。CI收听者尤其受到混响条件的影响，即使是小水平的混响也比大量的稳态噪声更大程度地降低理解力{Hazrati，2012 #674}。因此，减轻混响的影响的方法具有极大地改善CI用户的生活质量的潜力。以前试图解决耳蜗植入物的混响中的语音问题的尝试还不能在真实的时间中实现。我们的初步结果表明，成功缓解重叠掩蔽可以导致语音识别的大幅改善，即使自掩蔽没有减轻，我们已经设计了一种方法，可以在真实的时间实现。在所提出的努力中，我们将首先改进分类器，以基于我们成功的初步努力来检测混响。接下来，我们将评估缓解算法，首先在正常听力的听众中，然后在耳蜗植入的听众中。最后，我们将在真实的时间上实现该算法，并再次对其进行测试。