Commercial Readiness of a CI NR algorithm

CI NR 算法的商业准备情况

• 批准号: 10546391
• 负责人: David M Landsberger
• 金额: $ 102.07万
• 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/10546391
• 关键词: Address; Algorithms; Auditory; Bionics; Cellular Phone; Clinical; Cochlea; Cochlear Implants; Code; Collaborations; Communication; Comprehension; Data; Development; Devices; Ensure; Environment; Evaluation; Family; Feedback; Fostering; Hearing; Home; Intervention; Laboratories; Licensing; Manufacturer Name; Medical; Modification; Music; Noise; Patients; Performance; Persons; Phase; Positioning Attribute; Process; Property; Quality of life; Readiness; Restaurants; Risk; Signal Transduction; Small Business Innovation Research Grant; Speech; Speech Intelligibility; Technology; Testing; Time; Work; base; compare effectiveness; denoising; improved; portability; 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.

项目概要/摘要 人工耳蜗 (CI) 用户通常能够在安静的环境中保持对话。 然而，当多人同时讲话时，例如在大型家庭聚餐或在餐厅中， CI 用户很难参与对话，并且经常退出或回避这种情况。 理想情况下，从信号中去除背景说话者（“胡言乱语”）的 CI 算法将允许改进 理解力和对话参与度。尽管 CI 包含降噪 (NR) 算法， 当背景嘈杂时，这些算法不起作用。将喋喋不休的内容与前台讲话者分开 带来了两个重大挑战。首先，信号和噪声的频谱特性非常相似： 两者都是言语。其次，多说话者胡言乱语的频谱和时间特性随时间变化，并且是 因此很难预测。 尽管存在这些挑战，我们还是开发了一种极其有效的算法，称为 SEDA 来消除 潺潺。 SEDA 在所有信噪比 (SNR) 下提高了对胡言乱语中语音的理解 平均为 26 个百分点（或 38 个百分点，当相对于安静听力正常化时）。相比之下，一个 商业 NR 算法（来自 Advanced Bionics 的 ClearVoice）几乎没有提供任何可检测到的好处。 在第二阶段的成功中，我们实现了商业上可行的 SEDA 实施。尽管如此， 需要开展大量工作才能使 SEDA 做好商业准备。以下目标是在 与 CI 制造商合作，促进 SEDA 技术获得 CI 制造商的许可。 目标 1：在非多语听力情况下评估 SEDA。至少，SEDA 必须是有益的 如果要将其商业化地实施到 CI 中，则不会产生杂音，并且在其他收听情况下不会产生不利影响。 因此，我们将使用语音识别来评估 SEDA 在非 babble 听觉场景中的效果， 听众偏好和计算指标。 目标 2：询问 SEDA 相对于 CI 制造商的商业产品的优势。我们 将比较 SEDA 与 Advanced Bionics、MED-EL、Cochlear 和 Oticon 的 NR 的有效性 医学上关于在胡言乱语、白噪声和语音形状噪声中轻描淡写的语音。 目标 3：从 SEDA 的家庭评估中获取真实反馈。我们将送病人回家 与 SEDA 合作一个月，收集反馈并确定意外问题或聆听情况 已解决。 目标 4：量化计算权衡对 SEDA 性能的影响。我们将修改 SEDA 中用于调整计算要求的参数数量。使用计算指标 和语音识别，我们将评估这些变化对 SEDA 性能的影响。