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脑控增强听觉:在多说话人环境中对空间移动对话的增强

Brain-Controlled Augmented Hearing for Spatially Moving Conversations in Multi-Talker Environments.

机构信息

Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA.

Mortimer B. Zuckerman Mind Brain Behavior Institute, New York, NY, 10027, USA.

出版信息

Adv Sci (Weinh). 2024 Nov;11(41):e2401379. doi: 10.1002/advs.202401379. Epub 2024 Sep 9.

Abstract

Focusing on a specific conversation amidst multiple interfering talkers is challenging, especially for those with hearing loss. Brain-controlled assistive hearing devices aim to alleviate this problem by enhancing the attended speech based on the listener's neural signals using auditory attention decoding (AAD). Departing from conventional AAD studies that relied on oversimplified scenarios with stationary talkers, a realistic AAD task that involves multiple talkers taking turns as they continuously move in space in background noise is presented. Invasive electroencephalography (iEEG) data are collected from three neurosurgical patients as they focused on one of the two moving conversations. An enhanced brain-controlled assistive hearing system that combines AAD and a binaural speaker-independent speech separation model is presented. The separation model unmixes talkers while preserving their spatial location and provides talker trajectories to the neural decoder to improve AAD accuracy. Subjective and objective evaluations show that the proposed system enhances speech intelligibility and facilitates conversation tracking while maintaining spatial cues and voice quality in challenging acoustic environments. This research demonstrates the potential of this approach in real-world scenarios and marks a significant step toward developing assistive hearing technologies that adapt to the intricate dynamics of everyday auditory experiences.

摘要

专注于多个干扰说话者中的特定对话对于有听力损失的人来说具有挑战性。脑控辅助听力设备旨在通过使用听觉注意力解码 (AAD) 根据听众的神经信号增强注意力集中的语音来缓解这个问题。与依赖于固定说话者的简化场景的传统 AAD 研究不同,提出了一个涉及多个说话者在背景噪声中连续在空间中移动并轮流说话的现实 AAD 任务。从三位神经外科患者中采集了侵入性脑电图 (iEEG) 数据,他们专注于两个移动对话中的一个。提出了一种结合 AAD 和双耳说话人独立语音分离模型的增强型脑控辅助听力系统。分离模型在保留说话人空间位置的同时对说话人进行解混,并向神经解码器提供说话人轨迹,以提高 AAD 准确性。主观和客观评估表明,该系统在具有挑战性的声学环境中提高了语音可懂度并促进了对话跟踪,同时保持了空间线索和语音质量。这项研究展示了这种方法在现实场景中的潜力,并朝着开发适应日常听觉体验复杂动态的辅助听力技术迈出了重要一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ba/11538705/76e3619debc1/ADVS-11-2401379-g001.jpg

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