DMX: Enabling Blind Source Separation for Hearing Health Care

DMX：实现听力保健盲源分离

• 批准号: 8648615
• 负责人: RICHARD S GOLDHOR
• 金额: $ 51.23万
• 依托单位: SPEECH TECHNOLOGY/APPLIED RESEARCH CORP.
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-10 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8648615
• 关键词: Acoustics; Aging; Algorithms; Biological; Biomedical Research; Businesses; Communities; Complex; Complex Mixtures; Computer software; Custom; Development; Devices; Effectiveness; Electroencephalography; Electronics; Engineering; Ensure; Environment; Generations; Goals; Hand; Health; Healthcare; Hearing; Hearing Aids; Image; Imagery; Individual; Libraries; Life; Location; Magnetic Resonance Imaging; Masks; Measurement; Modeling; Movement; Noise; Output; Performance; Phase; Population; Process; Property; Pythons; Research; Research Personnel; Rest; Sales; Signal Transduction; Solutions; Source; Speed; System; Techniques; Testing; Time; Translating; blind; coping; data acquisition; distraction; fetal; graphical user interface; improved; information display; innovation; instrument; interest; loss of function; meetings; public health relevance; reconstruction; response; sensor; signal processing; sound

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.

描述(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试图获取的关键信息；其他来源是干扰数据获取的干扰。常见的声学环境就是一个重要的例子：美国老龄化人口中的相当一部分人难以应对嘈杂的环境。目前的解决方案仅限于助听器，它以放大所有声音而臭名昭著，或者耳机，它选择性地放大单一来源，但将听者与其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将承载信息的信号从干扰掩蔽物中分离出来，从而使与健康相关的应用程序的开发成为可能。我们建议开发一种名为DMX的系统，该系统使用创新的信号处理技术，从通常与未知的同时源混合物响应的多个传感器的输出中分离和提取(即“分离”)单个声源和生物电源信号。我们已经实现的核心DMX算法，以及我们在第一阶段中演示的其有效性，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。一项经过验证的DMX创新是使用“标记器”。标记器是连接到系统识别的重要目标或掩蔽源的传感器。其他传感器检测远程(未标记)目标或噪声源。我们目前的DMX算法构成了一个通用的盲源分离(BSS)算法，它推动了最先进的技术水平。在第二阶段，我们建议将该算法打包为一个经过充分测试、有文档记录、受支持且可部署的软件库，该软件库具有MATLAB、C++和Python接口。该图书馆将向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、肌电等-TO 允许研究人员和临床医生从反应混合物(例如胎儿和产妇的心跳)中分离出感兴趣的来源。我们还将开发和销售交钥匙DMX仪器，包括多达8个麦克风、信号处理电子设备和控制软件。这一版本的DMX将对那些需要在嘈杂环境中产生高保真低噪音录音的研究人员有用，这些人包括核磁共振扫描仪，以及不是音频或生物电信号工程师的人。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，我们的目标是通过使用动态误差分析、智能使用环境信息(如源到传感器的距离信息)以及重复使用先前生成的“分离解决方案”来减少源分离时间。这两个版本的DMX产品将在项目结束时准备好投入商业使用。