Clarity in Motion: A Motion-Tolerant Aid for Selectively Hearing Acoustic Sources

带有多个移动声源的嘈杂房间给听力受损的听众带来了问题。 不想要的掩蔽声音降低了语言的可理解性，以及听众想要听到的其他声音。“来源 已知可提取重要源并“清除”不需要的噪声的“分离”信号处理方法， 但这些方法通常需要修复它们处理的声学传感器(麦克风)和信号源 在空间中-由这种方法计算的最优分离解与位置有关。运动 降低计算的分离解决方案的分离质量(Qos)，并在 改变位置需要时间--通常是几十秒。这种限制限制了传统的 分离方法。我们提出了一种新的辅助听力系统CIM(Clarity In Motion)，它是 能够在不断变化的真实环境中保持声源的最佳分离 “人类”速度。CIM大大缩短了重新汇聚分离解决方案所需的时间。CIM是 旨在集成到NIH的助听器和个人音频的开放语音平台(OSP)计划中 设备。CIM利用STAR的多算法源代码分离(MASS)应用框架 可插拔的声学分离模块。MASS与OSP兼容，并在GitHub上公开提供。 CIM是以房间为中心、基于传感器图像和特定于监听者的。重要的系统组件有 嵌入房间本身，而不是用户的耳朵(例如助听器)。CIM提供特定于听众的音频 通过他们的智能手机发送给一个或多个用户。CIM使用多个麦克风分布在一个房间内 并连接到支持所有听众的CIM Room服务器(信号处理设备)。此服务器PRO- 接收来自这些共享房间麦克风的音频信号，以清除来自私人监听者的不需要的声音 麦克风，通常是助听器、人工耳机或其他特定于每个听众的头戴式麦克风。 每个听众使用CIM移动设备应用程序注册他们的听众麦克风，并指定要 擦洗。房间服务器为每个监听者计算个性化的已擦除音频流并将其传输 无线连接到他们的Listener App。Listener App将该流输出到收听者的助听器Cochlear 植入，或作为标准线路电平或电流环路音频信号的耳塞。 CIM创新的核心在于这里描述的两种独立的专有技术，用于 减少与源或传感器移动相关的分离溶液退缩(ΔQ)。 在第一阶段，我们将描述ΔQ与相关客观参数之间的关系 声学场景；实施并定量评估我们的新方法对减少 以及对运动与运动之间的关系进行感性研究-- 诱导解决方案的去偏转，以及听力努力和可理解性判断。 CIM系统将帮助听力受损的听众在有移动信号源的嘈杂房间里听得清楚。

Noisy rooms with multiple moving sound sources create problems for hearing-impaired listeners. Unwanted masking sounds reduce the intelligibility of speech and other sounds listeners want to hear. “Source Separation” signal processing methods are known that extract important sources and “scrub” unwanted noise, but these methods typically require the acoustic sensors (microphones) and sources they process to be fixed in space—the optimal separation solutions computed by such methods are position dependent. Movement degrades the quality of separation (QoS) of the computed separation solutions, and reconvergence following a change of position takes time—often tens of seconds. This constraint limits the practical utility of traditional separation methods. We propose a novel assistive listening system called CIM (“Clarity in Motion”) which is capable of maintaining an optimal separation of acoustic sources in real-world environments changing at “human” speeds. CIM dramatically shortens the time required to reconverge separation solutions. CIM is designed for integration into NIH’s Open Speech Platform (OSP) initiative for hearing aids and personal audio devices. CIM leverages STAR’s Multiple Algorithm Source Separation (MASS) application framework of “pluggable” acoustic separation modules. MASS is compatible with OSP and is publicly available on GitHub. CIM is room-centric, sensor image-based, and listener-specific. Important system components are embedded in the room itself, rather than in the user’s ear (e.g. hearing aid). CIM delivers listener-specific audio to one or more users through their smartphones. CIM employs multiple microphones distributed around a room and connected to a CIM Room Server (a signal processing device) supporting all listeners. This Server pro- cesses the audio signals from these shared Room Mics to scrub unwanted sounds from private Listener Mics, which are typically hearing aid, cochlear implant, or other head-mounted mics specific to each listener. Each listener uses a CIM mobile device app to register their Listener Mic and specify which acoustic sources to scrub. The Room Server computes an individualized scrubbed audio stream for each listener and transmits it wirelessly to their Listener App. The Listener App outputs this stream to the listener’s hearing aid, cochlear implant, or earbuds as a standard line level or current loop audio signal. The heart of CIM’s innovation resides in two separate proprietary techniques, described herein, for reducing the separation solution deconvergence (ΔQ) associated with source or sensor movements. In Phase I, we will characterize the relationship between ΔQ and relevant objective parameters of acoustic scenes; implement and quantitatively evaluate the contribution of our novel methods for reducing motion-induced deconvergence; and carry out a perceptual study of the relationship between movement- induced solution deconvergence and both listening effort and intelligibility judgements. The CIM system will help hearing-impaired listeners hear clearly in noisy rooms with moving sources.