Enabling Technology for Separation and Enhancement of Mixed Signals

描述（由申请人提供）：我们建议开发一个系统，从响应多个同时源的传感器输出中分离和提取单个生物电和声源。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，从而使其他与健康相关的应用程序的开发成为可能。我们的系统将通过分离竞争的前景信号源、抑制背景信号源、识别和去除结果中的回声和类似效果来实时“清理”实时信号。它将采用多个传感器和算法，从嘈杂环境中提取单个源，并确定源方向和反射面等环境特征。我们系统的一个创新之处在于，一些传感器被用来“标记”已知的资源。标签传感器附着在系统识别的重要目标或掩蔽源上。其他传感器用于拾取背景噪声和远程（未标记）目标或掩蔽源。该系统将使用“盲源分离”技术，通过标记传感器和关于源和环境的简单、一般信息，提供高级功能。这将使研究人员能够减少对数据收集和应对环境的细节的关注，而更多地关注源本身或它们的位置和信号信息。在第一阶段，我们将测试分离算法，并观察其使用和不使用标签传感器的性能。对于需要在嘈杂环境中（如MRI扫描仪）创建高保真低噪声记录的研究人员，以及非音频或生物电信号工程师，该系统将非常有用。它将允许用户标记最突出的信号源，记录整个“信号场景”，并提取所需的信号源信号和相关位置信息。我们的系统的第二个重要用途是作为轻度到中度听力损失的人的辅助听力设备，使他们在嘈杂的社交场合，如会议、餐馆和会议中有效地发挥作用。有了合适的传感器，该系统将适用于生物电信号——脑电图、肌电图等——使研究人员和临床医生能够分别研究胎儿和母亲的心跳，包括波形模式和相应源的位置。

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.