DMX: Enabling Blind Source Separation for Hearing Health Care

描述（由申请人提供）：典型的生物环境包括来自多种生物和环境来源的信号的复杂混合物。一些来源包含研究人员寻求获取的关键信息;其他来源是干扰数据获取的干扰因素。常见的声学环境就是一个重要的例子：美国老龄化人口中有很大一部分难以应对嘈杂的环境。目前的解决方案仅限于助听器，众所周知，助听器放大所有声音，或耳机，选择性地放大单个源，但将听者与他或她的声学环境的其余部分隔离。我们的目标是通过提供一个软件库和一个交钥匙工具，使生物医学研究人员能够轻松地从干扰掩蔽物中分离出信息承载信号，从而实现与健康相关的应用程序的开发。 我们建议开发一种称为DMX的系统，该系统使用创新的信号处理技术从多个传感器的输出中分离和提取（即“解混”）单个声学和生物电源信号，这些传感器通常响应于未知的同时源混合物。我们已经实现的核心DMX算法，其有效性我们在第1阶段中证明，“清理”现场信号在真实的时间，分离竞争的前景来源，并抑制背景噪声。DMX的一项经过验证的创新是使用“标记器”。标签器是一种传感器，连接到系统识别的重要目标或掩蔽源。其他传感器检测远程（未标记的）目标或噪声源。 我们目前的DMX算法构成了一个通用的“盲源分离”（BSS）算法，它推进了最先进的技术水平。在第2阶段，我们建议将该算法打包为一个经过充分测试、有文档记录、受支持和可部署的软件库，并提供MATLAB、C++和Python接口。该图书馆将为研究界以及辅助听力设备的设计者提供可靠的BSS能力。该库还将适用于处理生物电信号- EEG，EMG等-以 允许研究人员和临床医生从反应混合物中分离感兴趣的来源（例如胎儿和母体心跳）。 我们还将开发和销售交钥匙DMX仪器，包括多达八个麦克风、信号处理电子设备和控制软件。此版本的DMX对于需要在MRI扫描仪等嘈杂环境中制作高保真低噪声录音的研究人员以及非音频或生物电信号工程师的研究人员非常有用。该仪器将允许这样的用户标记最突出的源，记录整个“信号场景”，并提取单独的源信号和相关的位置信息。 在第二阶段，我们的目标是减少源分离时间，采用动态误差分析，智能使用环境信息，如源到传感器的距离信息，并重用以前生成的“分离解决方案”。 DMX产品的两个版本都将在项目结束时投入商业使用。

DESCRIPTION (provided by applicant): Typical biological environments comprise complex mixtures of signals from multiple biological and environmental sources. Some sources contain critical information that researchers seek to acquire; other sources are distractions that interfer with data acquisition. Common acoustic environments are an important example: a significant segment of the aging US population has difficulty coping with noisy settings. Current solutions are limited to hearing aids, which notoriously amplify all sounds, or headsets, which selectively amplify a single source but isolate the listener from the rest of his or her acoustic environment. Our goal is to enable the development of health-related applications by providing a software library and a turn-key instrument that enable biomedical researchers to easily isolate information-bearing signals from interfering maskers. We propose to develop a system called DMX that uses innovative signal processing techniques to isolate and extract (that is, "demix") individual acoustic and bioelectrical source signals from the output of multiple sensors that are generally responding with an unknown mixture of simultaneous sources. The core DMX algorithm we have implemented, and whose effectiveness we demonstrated in Phase 1, "cleans up" live signals in real time by separating competing foreground sources, and suppressing background noise. A proven DMX innovation is the use of "taggers". A tagger is a sensor attached to a significant target or masking source that is identified to the system. Other sensors detect remote (untagged) targets or noise sources. Our current DMX algorithm constitutes a general-purpose "blind source separation" (BSS) algorithm that advances the state of the art. In Phase 2, we propose to package this algorithm as a fully tested, documented, supported, and deployable software library with MATLAB, C++, and Python interfaces. The library will provide reliable BSS capability to the research community, as well as to designers of assistive listening devices. The library will also be suitable for processing bioelectric signals - EEG, EMG, etc. - to allow researchers and clinicians to isolate sources of interest from response mixtures (e.g. fetal and maternal heartbeats). We will also develop and sell a turn-key DMX instrument, complete with up to eight microphones, signal processing electronics, and control software. This version of DMX will be useful to researchers who need to produce high-fidelity low-noise recordings in noisy environments such as MRI scanners, and who are not audio or bioelectrical signal engineers. This instrument will allow such a user to tag the most prominent sources, record the entire "signal scene", and extract the separate source signals and related location information. In Phase 2 we aim to reduce source separation time by employing dynamic error analysis, the intelligent use of environmental information such as source-to-sensor distance information, and the reuse of previously generated "separation solutions". Both versions of the DMX product will be ready for commercial use by the end of the project.