Zhou Huali, Wang Ningyuan, Zheng Nengheng, Yu Guangzheng, Meng Qinglin
Acoustics Lab, School of Physics and Optoelectronics, South China University of Technology, Guangzhou, China.
Nurotron Biotechnology Inc., Hangzhou, China.
Front Neurosci. 2020 Apr 21;14:301. doi: 10.3389/fnins.2020.00301. eCollection 2020.
The cochlea "translates" the in-air vibrational acoustic "language" into the spikes of neural "language" that are then transmitted to the brain for auditory understanding and/or perception. During this intracochlear "translation" process, high resolution in time-frequency-intensity domains guarantees the high quality of the input neural information for the brain, which is vital for our outstanding hearing abilities. However, cochlear implants (CIs) have coarse artificial coding and interfaces, and CI users experience more challenges in common acoustic environments than their normal-hearing (NH) peers. Noise from sound sources that a listener has no interest in may be neglected by NH listeners, but they may distract a CI user. We discuss the CI noise-suppression techniques and introduce noise management for a new implant system. The monaural signal-to-noise ratio estimation-based noise suppression algorithm "eVoice," which is incorporated in the processors of Nurotron Enduro, was evaluated in two speech perception experiments. The results show that speech intelligibility in stationary speech-shaped noise can be significantly improved with eVoice. Similar results have been observed in other CI devices with single-channel noise reduction techniques. Specifically, the mean speech reception threshold decrease in the present study was 2.2 dB. The Nurotron society already has more than 10,000 users, and eVoice is a start for noise management in the new system. Future steps on non-stationary-noise suppression, spatial-source separation, bilateral hearing, microphone configuration, and environment specification are warranted. The existing evidence, including our research, suggests that noise-suppression techniques should be applied in CI systems. The artificial hearing of CI listeners requires more advanced signal processing techniques to reduce brain effort and increase intelligibility in noisy settings.
耳蜗将空气中振动的声学“语言”“翻译”成神经“语言”的脉冲，然后将这些脉冲传输到大脑进行听觉理解和/或感知。在这个耳蜗内的“翻译”过程中，时频强度域的高分辨率保证了输入大脑的神经信息的高质量，这对于我们出色的听力能力至关重要。然而，人工耳蜗（CI）具有粗糙的人工编码和接口，与听力正常（NH）的同龄人相比，CI使用者在普通声学环境中面临更多挑战。NH听众可能会忽略来自他们不感兴趣声源的噪声，但这些噪声可能会分散CI使用者的注意力。我们讨论了CI噪声抑制技术，并介绍了一种新植入系统的噪声管理方法。在两项语音感知实验中，对纳入诺尔康Enduro处理器的基于单耳信噪比估计的噪声抑制算法“eVoice”进行了评估。结果表明，使用eVoice可以显著提高在平稳语音形状噪声中的语音清晰度。在其他采用单通道降噪技术的CI设备中也观察到了类似结果。具体而言，本研究中平均语音接收阈值降低了2.2dB。诺尔康协会已有超过10000名用户，eVoice是新系统中噪声管理的一个开端。未来在非平稳噪声抑制、空间声源分离、双耳听力、麦克风配置和环境识别方面还有待进一步开展工作。现有证据，包括我们的研究，表明噪声抑制技术应应用于CI系统。CI使用者的人工听力需要更先进的信号处理技术，以减少大脑负担并提高在嘈杂环境中的清晰度。
