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

具有多个移动声源的嘈杂房间会给听力受损的听众带来问题。 不需要的掩蔽声音降低了语音的可懂度和听众想要听到的其他声音。“来源 已知“分离”信号处理方法提取重要的源并“去除”不想要的噪声， 但是这些方法通常要求声传感器（麦克风）和它们处理的声源是固定的 在空间中-通过这种方法计算的最佳分离解是位置相关的。运动 降低了计算出的分离解的分离质量（QoS），并且在a之后重新收敛 位置的改变需要时间-通常是几十秒。这一限制限制了传统方法的实际效用。 分离方法我们提出了一种新的辅助听力系统，称为CIM（“清晰的运动”）， 能够在实际环境中保持声源的最佳分离， “人”的速度。CIM大大缩短了重新融合分离解决方案所需的时间。CIM是 设计用于集成到NIH的开放语音平台（OSP）计划，用于助听器和个人音频 装置. CIM利用星星的多算法源分离（MASS）应用程序框架， “可插拔”声分离模块。MASS与OSP兼容，并在GitHub上公开提供。 CIM以房间为中心，基于传感器图像，并且特定于制造商。重要的系统组件包括 嵌入在房间本身，而不是在用户的耳朵（例如助听器）。CIM提供特定于听众的音频 一个或多个用户通过他们的智能手机。CIM使用多个麦克风分布在房间周围 并且连接到支持所有收听者的CIM房间服务器（信号处理设备）。此服务器Pro- 从这些共享的房间麦克风中删除音频信号，以清除私人麦克风中不需要的声音 麦克风，通常是助听器、人工耳蜗或其他针对每个听众的头戴式麦克风。 每个听众都使用CIM移动终端应用程序注册他们的麦克风，并指定要 擦洗。房间服务器为每个听众计算个性化的擦洗音频流并将其传输 无线传输到他们的助听器应用程序。助听器应用程序将此流输出到听者的助听器，耳蜗 植入物或耳塞作为标准线路电平或电流环路音频信号。 CIM创新的核心在于本文所述的两种独立的专有技术， 减小与源或传感器移动相关联的分离溶液去收敛（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;实施和定量评估我们的新方法的贡献，减少 运动引起的去会聚;并对运动之间的关系进行知觉研究- 诱导的解决方案反收敛和听力努力和可理解性判断。 CIM系统将帮助听障人士在有移动声源的嘈杂房间里听得更清楚。

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.