Commercial Readiness of a CI NR algorithm

CI NR 算法的商业准备情况

• 批准号: 10672315
• 负责人: David M Landsberger
• 金额: $ 98.96万
• 依托单位: YORK SOUND INC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-25 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10672315
• 关键词: Address; Algorithms; Auditory; Bionics; Cellular Phone; Clinical; Cochlea; Cochlear Implants; Code; Collaborations; Communication; Comprehension; Data; Development; Devices; Environment; Evaluation; Family; Feedback; Fostering; Hearing; Home; Intervention; Laboratories; Licensing; Manufacturer; Medical; Modification; Music; Noise; Patients; Performance; Persons; Phase; Positioning Attribute; Process; Property; Quality of life; Readiness; Restaurants; Risk; Shapes; Signal Transduction; Small Business Innovation Research Grant; Speech; Speech Intelligibility; Technology; Testing; Time; Work; compare effectiveness; denoising; improved; portability; prediction algorithm; preference; sound; speech recognition

PROJECT SUMMARY / ABSTRACT Cochlear implant (CI) users are typically able to maintain conversations in quiet environments. However, when multiple people are talking simultaneously, such as at a large family dinner or in a restaurant, CI users have great difficulty participating in conversations and frequently withdraw or avoid the situation. Ideally, CI algorithms to remove background talkers (“babble”) from the signal will allow for improved comprehension and conversational engagement. Although CIs incorporate noise reduction (NR) algorithms, these algorithms are not effective when the background is babble. Separating babble from a foreground talker poses two significant challenges. First, the spectral properties of the signal and noise are extremely similar as both are speech. Second, the spectral and temporal properties of multi-talker babble change with time and are therefore difficult to predict. Despite these challenges, we developed an extremely effective algorithm called SEDA to remove babble. SEDA improved understanding of speech in babble at all signal-to-noise ratios (SNRs) tested by an average of 26 percentage points (or 38 points, when normalized with respect to hearing in quiet). In contrast, a commercial NR algorithm (ClearVoice from Advanced Bionics) provided little to no detectable benefit. In a successful Phase 2, we produced a commercially viable implementation of SEDA. Nevertheless, significant work is required to bring SEDA to commercial readiness. The Aims below were developed in conjunction with CI manufacturers to facilitate SEDA technology for licensing by CI manufacturers. Aim 1: Evaluate SEDA in non-babble listening situations. At minimum, SEDA must be beneficial with babble and not detrimental in other listening situations if it is to be commercially implemented into a CI. Therefore, we will evaluate the effect of SEDA in non-babble auditory scenes using speech recognition, listener preference, and a computational metric. Aim 2: Interrogate benefits of SEDA relative to commercial offerings from CI manufacturers. We will compare the effectiveness of SEDA with NR from Advanced Bionics, MED-EL, Cochlear, and Oticon Medical on understating speech in babble, white, and speech-shaped noise. Aim 3: Obtain real-world feedback from at home evaluations of SEDA. We will send patients home for a month with SEDA to collect feedback and to ascertain unexpected issues or listening situations to be addressed. Aim 4: Quantify the effects of computational trade-offs on SEDA performance. We will modify the number of parameters used in SEDA to adjust the computational requirements. Using a computational metric and speech recognition, we will evaluate the effects of the of these changes on SEDA’s performance.

项目摘要 /摘要 人工耳蜗（CI）用户通常能够在安静的环境中维护对话。 但是，当多人同时交谈时，例如在大型家庭晚餐或餐厅里 CI用户参与对话和自由撤回或避免情况有很大的差异。 理想情况下，从信号中删除背景说话者（“ babble”）的CI算法允许改进 理解和转换参与。 当背景与前景分开时，这些算法不是效率的。 首先提出两个重大挑战。 两者都是语音。 因此很难预测。 尽管面临挑战，但我们开发了一种极其刻板的算法，称为SEDA来删除 Babble。 平均为26个百分点（或相对于安静的听力，分别为38点） 商业NR算法（来自Advanced Bionics的ClearVoice）几乎没有探测器的利益。 在第2阶段的性行为中，我们制作了SEDA的商业可行实施。 需要大量工作才能使以下目的发展。 与制造商共同促进CI制造商许可的SEDA技术。 AIM 1：在非吸引人的聆听情况下评估SEDA。 在其他听力中，有害而不是有害的，它将在CI中商业实施。 因此，我们将使用语音识别，在非潜在的听觉场景中评估SEDA的效果， 听众的偏好和计算机指标。 AIM 2：询问SEDA对CI制造商的商业产品的好处 将比较SEDA与Advanced Bionics，Med-El，Cochlear和Ocoton的NR的有效性 医疗言论在辣椒，白色和言语形状的噪音中低估。 AIM 3：从SEDA的家庭评估中获取现实世界的反馈。 与SEDA一起收集反馈并确定意外问题或聆听情况是 解决。 目标4：量化计算权衡对SEDA性能的影响。 SEDA中使用的参数数量用于调整计算指标。 和语音识别，我们将评估这些对这些对SEDA性能的影响。