Enabling Technology for Separation and Enhancement of Mixed Signals

混合信号分离和增强的支持技术

• 批准号: 7802403
• 负责人: Joel M MacAuslan
• 金额: $ 16.15万
• 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 2012-09-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7802403
• 关键词: Acoustics; Algorithms; Braces-Orthopedic appliances; Characteristics; Collection; Data; Data Collection; Development; Devices; Electrocardiogram; Electroencephalography; Encapsulated; Engineering; Environment; Exercise; Futility; Individual; Infant; Life; Location; MRI Scans; Magnetic Resonance Imaging; Masks; Modeling; Morphologic artifacts; Motion; Noise; Output; Pattern; Performance; Persons; Phase; Positioning Attribute; Process; Research Personnel; Restaurants; Signal Transduction; Source; Surface; System; Techniques; Technology; Testing; Time; Toy; base; blind; computerized data processing; coping; fetal; flexibility; hearing impairment; improved; innovation; interest; meetings; novel; public health relevance; sensor; social; software development; sound; symposium; transmission process; vocalization

DESCRIPTION (provided by applicant): We propose to develop a system to isolate and extract individual bioelectrical and acoustic sources from the output of sensors that are responding to multiple simultaneous sources. The purpose of the system is to enable the development of other health-related applications by allowing researchers to focus on the applications instead of the details of signal collection and analysis. Our system will "clean up" live signals in real time by separating competing foreground sources, suppressing background sources, and identifying and removing echoes and similar effects from the results. It will employ multiple sensors with algorithms to extract individual sources from noisy environments, and to determine source directions and environment characteristics such as reflecting surfaces. An innovation in our system is that some sensors are used to "tag" known sources. Tagging sensors are attached to significant target or masking sources that are identified to the system. Other sensors are used to pick up background noise and remote (untagged) target or masking sources. The system will provide high-level functionality through tagging sensors and simple, general information about the sources and the environment, using techniques of "blind source separation". This will allow researchers to focus less on details of the data collection and coping with the environment, and more on the sources themselves or their positional and signal information. In Phase 1, we will test the separation algorithm and observe its performance with and without tagging sensors. The proposed system would be useful to researchers who need to create high-fidelity low- noise recordings in noisy environments such as MRI scanners, and who are not audio or bioelectrical-signal engineers. It would allow a user to tag the most prominent sources, record the entire "signal scene", and extract the desired source signals and related location information. A second important use of our system would be as an assistive listening device for persons with mild to moderate hearing loss, allowing them to function effectively in noisy social situations such as meetings, restaurants, and conferences. With appropriate sensors, the system will be suitable for use with bioelectric signals - EEG, EMG, etc. - to allow researchers and clinicians to study fetal and maternal heartbeats separately, both for waveform patterns and for the locations of the corresponding sources. PUBLIC HEALTH RELEVANCE: We propose to develop a system to isolate and extract individual signal sources, whether bioelectrical (EEG, ECG) or acoustic, to enable the development of other health-related applications. An important use of our system would be as an assistive listening device for persons with mild to moderate hearing loss, allowing them to function effectively in noisy social situations such as meetings and restaurants. It would also be useful to researchers who need to create high-fidelity low-noise recordings in noisy environments such as MRI scanners. It would be equally suitable for separating bioelectrical signals such as fetal and maternal heartbeats, and providing location information for each of the sources.

描述（由申请人提供）：我们建议开发一个系统，以隔离和提取对多个同时源的传感器的输出，将单个的生物电气和声学源隔离。该系统的目的是通过允许研究人员专注于应用程序而不是信号收集和分析的细节来实现其他与健康相关的应用程序的开发。我们的系统将通过分开竞争的前景来源，抑制背景来源，识别和删除回声和类似结果的效果，从而实时“清理”实时信号。它将采用多个具有算法的传感器从嘈杂的环境中提取单个来源，并确定源方向和环境特征，例如反射表面。我们系统中的创新是一些传感器用于“标记”已知来源。标记传感器连接到确定到系统的重要目标或掩盖源。其他传感器用于拾取背景噪声和远程（未标记）目标或掩盖源。该系统将使用“盲源分离”技术通过标记传感器以及有关来源和环境的简单一般信息来提供高级功能。这将使研究人员减少关注数据收集的细节，并应对环境，更多地关注来源本身或其位置和信号信息。在第1阶段，我们将测试分离算法，并在没有标记传感器的情况下观察其性能。提出的系统对于需要在MRI扫描仪等嘈杂环境中创建高保真性低噪声记录的研究人员很有用，而不是音频或生物电信号工程师。它将允许用户标记最突出的来源，记录整个“信号场景”，并提取所需的源信号和相关的位置信息。我们系统的第二个重要用途是作为轻度至中度听力损失的人的辅助聆听设备，使他们能够在嘈杂的社交场合（例如会议，餐馆和会议）中有效运作。使用适当的传感器，该系统适合与生物电信号（EEG，EMG等）一起使用，以使研究人员和临床医生能够分别研究胎儿和母体心跳，包括波形模式以及相应来源的位置。 公共卫生相关性：我们建议开发一种系统来隔离和提取单个信号源，无论是生物电（EEG，ECG）还是声学，以实现其他与健康相关的应用程序的开发。我们系统的一个重要用途是作为轻度至中度听力损失的人的辅助聆听设备，使他们能够在嘈杂的社交场合（例如会议和餐馆）中有效运作。对于需要在MRI扫描仪等嘈杂环境中创建高保真性低噪声录音的研究人员也将很有用。它同样适合分离生物电信号，例如胎儿和母体心跳，并为每个来源提供位置信息。