Removing background talker noise for cochlear implant users

为人工耳蜗用户消除背景说话者噪音

• 批准号: 10009945
• 负责人: David M Landsberger
• 金额: $ 78.76万
• 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 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10009945
• 关键词: Address; Algorithms; Auditory; Bionics; Buffers; Cellular Phone; Clinical; Cochlear Implants; Code; Communication; Data Collection; Devices; Effectiveness; Environment; Evaluation; Family; Fourier Transform; Goals; Hearing; Hearing Aids; Life; Manufacturer Name; Modification; Names; Noise; Periodicity; Property; Quality of life; Research; Restaurants; Signal Transduction; Speech; Speech Perception; System; Testing; Time; Visual; commercialization; denoising; hearing impairment; improved; preference; prototype; signal processing; sound; speech in noise; virtual

PROJECT SUMMARY / ABSTRACT When hearing-impaired listeners are properly aided with a hearing aid (HA) or cochlear implant (CI), they are often able to comfortably maintain a conversation in quiet environments. However, in group environments, such as a large family dinner, restaurant, or other environment where multiple people are talking simultaneously, hearing-impaired listeners have great difficulty participating in conversations and frequently withdraw or avoid the situation. As such, it would be highly beneficial to implement an algorithm into HAs or CIs to remove background talkers (“babble”) from the signal to reduce listening effort for the hearing-impaired listener and allow them to converse as if they were in a quiet environment. Although HAs and CIs frequently incorporate noise reduction algorithms, these algorithms are not effective when the background is babble. The problem of removing babble involves segregating speech from speech. Hence, the spectral properties of the signal and noise are extremely similar. Despite these challenges, we developed an extremely effective algorithm named SEDA to remove background babble. A prototype of SEDA was implemented on an iPhone and evaluated on 10 CI users. SEDA improved understanding of speech with background talkers at all signal-to-noise ratios (SNRs) tested; on average, word understanding in babble improved by 31 percentage points. By contrast, the state-of-the-art noise reduction systems for CIs provide little to no benefit for understanding speech with babble noise. CI manufacturers have shown great enthusiasm about our successful proof-of-concept of our algorithm. Nevertheless, before commercialization, CI manufacturers want reductions in the computational power required for the algorithm. As CI processors minimize computational processing in order to maximize battery life, it is important to minimize the additional computations required by SEDA. When using SEDA as a front- end for a CI processing strategy (as is the case with our iPhone prototype), redundancy in the required calculations result in increased computations and latency. Specifically, SEDA decomposes the input signal into multiple channels, removes the background babble, and then reassembles them into a single waveform. This waveform is then fed into a CI which again decomposes the signal into multiple channels. Integrating SEDA into the signal processing chain will save computational processing as the signal would only need to be decomposed once and would not need to be reassembled. Additionally, although SEDA is highly successful in typical speech in noise tests, CI manufacturers emphasized the importance of evaluating SEDA in more realistic environments. Two specific aims will address the requirements for commercialization by the CI manufactures: reducing the computational requirements by integrating SEDA into a sound processing algorithm and evaluating SEDA in realistic environments.

项目概要/摘要 当听力受损的听众得到助听器 (HA) 或人工耳蜗 (CI) 的适当帮助时， 然而，在群体中，他们通常能够在安静的环境中轻松地保持对话。 环境，例如大型家庭聚餐、餐厅或其他多人交谈的环境 同时，听力受损的听众很难参与对话，并且经常 因此，在 HA 或 CI 中实施算法将非常有益。 从信号中消除背景说话者（“胡言乱语”），以减少听力障碍者的聆听努力 尽管 HA 和 CI 经常发生，但允许他们像在安静的环境中一样交谈。 结合降噪算法，当背景嘈杂时这些算法不起作用。 消除胡言乱语的问题涉及将语音与语音分离，因此，需要考虑语音的频谱特性。 信号和噪声极其相似。 尽管存在这些挑战，我们还是开发了一种极其有效的算法，名为 SEDA 来消除 SEDA 原型在 iPhone 上实现，并对 10 位 CI 用户进行了评估。 SEDA 在所有测试的信噪比 (SNR) 下提高了对背景说话者语音的理解； 相比之下，babble 中的单词理解能力平均提高了 31 个百分点。 CI 的降噪系统对于理解含杂音的语音几乎没有任何帮助。 CI 制造商对我们算法的成功概念验证表现出了极大的热情。 然而，在商业化之前，CI 制造商希望降低计算能力 CI 处理器最大限度地减少计算处理，以最大限度地提高电池电量。 当使用 SEDA 作为前端时，尽量减少 SEDA 所需的额外计算非常重要。 CI 处理策略结束（就像我们的 iPhone 原型的情况），所需的冗余 具体来说，SEDA 将输入信号分解为。 多个通道，去除背景杂音，然后将它们重新组合成单​​个波形。 然后将波形输入 CI，CI 再次将信号分解为多个通道并积分 SEDA。 进入信号处理链将节省计算处理，因为信号只需要 尽管 SEDA 在这方面非常成功，但分解一次后无需重新组装。 噪音测试中的典型言论，CI厂商更强调评估SEDA的重要性 现实环境。 两个具体目标将满足 CI 制造商的商业化要求： 通过将 SEDA 集成到健全的处理算法中来减少计算需求 在现实环境中评估 SEDA。