Commercial Readiness of a CI NR algorithm

项目摘要/摘要 人工耳蜗(CI)使用者通常能够在安静的环境中保持对话。 然而，当多个人同时交谈时，例如在大型家庭聚餐或在餐馆里， CI用户在参与对话方面有很大困难，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， 当背景杂乱无章时，这些算法是无效的。将胡言乱语与前台说话者分开 带来了两个重大挑战。首先，信号和噪声的光谱特性非常类似于 两者都是演讲。第二，多人讲话的频谱和时间特性随时间而变化，并且 因此很难预测。 尽管有这些挑战，我们还是开发了一种非常有效的算法，称为SEDA来删除 胡言乱语。SEDA提高了在所有信噪比(SNR)下对BUBLE中的语音的理解 平均26个百分点(或38个百分点，在安静的听力方面归一化后)。相比之下，一个 商业NR算法(来自Advanced Bionics的ClearVoice)提供的好处很少，甚至没有。 在成功的第二阶段，我们生产了一个商业上可行的SEDA实现。不过， 要使SEDA进入商业准备状态，需要做大量的工作。下面的目标是在 与CI制造商合作，促进CI制造商获得许可的SEDA技术。 目标1：在不能胡言乱语的情况下评估SEDA。至少，SEDA必须是有益的 如果要将其商业实施到CI中，则在其他收听情况下不会有害。 因此，我们将使用语音识别来评估SEDA在非嘈杂听觉场景中的效果， 收听者偏好和计算指标。 目标2：询问SEDA相对于CI制造商的商业产品的好处。我们 将比较来自Advanced Bionics、MED-EL、Cochlear和Oticon的SEDA和NR的有效性 医学上的轻描淡写的讲话中的胡言乱语，白色，和讲话形状的噪音。 目标3：从家庭对SEDA的评估中获得真实世界的反馈。我们会把病人送回家 与SEDA一起工作一个月，以收集反馈并确定意外问题或听证情况 地址。 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。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。

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.