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个生物和环境源的信号的复杂混合。一些来源包含研究人员试图获取的关键信息；其他来源是干扰数据采集的干扰。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，它可以放大所有声音，或者耳机，它可以选择性地放大单个声源，但将听者与其他声音环境隔离开来。我们的目标是通过提供软件库和交钥匙仪器，使生物医学研究人员能够轻松地将承载信息的信号与干扰掩蔽器隔离开来，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，这些传感器通常以未知的同时源混合响应。我们已经实现的核心DMX算法，其有效性我们在第一阶段展示了，通过分离竞争的前景源和抑制背景噪声来实时“清理”实时信号。DMX的一个成熟创新是“标签”的使用。标记器是附着在系统识别的重要目标或掩蔽源上的传感器。其他传感器检测远程（未标记）目标或噪声源。我们目前的DMX算法构成了一种通用的“盲源分离”（BSS）算法，这是目前最先进的算法。在第二阶段，我们建议将该算法打包为一个经过充分测试、记录、支持和可部署的软件库，并使用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-脑电图，肌电图等

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.