Enabling Technology for Separation and Enhancement of Mixed Signals

描述(由申请人提供)：我们建议开发一种系统，从响应多个同时源的传感器的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将重点放在应用程序上，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源、抑制背景源、识别和去除结果中的回声和类似效果来实时地“清理”实时信号。它将使用多个传感器和算法从噪声环境中提取单个信号源，并确定信号源方向和环境特征，如反射面。我们系统的一项创新是，一些传感器被用来“标记”已知的来源。标记传感器连接到系统识别的重要目标或掩蔽源。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。该系统将通过标记传感器以及关于源和环境的简单、一般信息，使用“盲源分离”技术来提供高级功能。这将使研究人员能够更少地关注数据收集和应对环境的细节，而更多地关注源本身或其位置和信号信息。在第一阶段，我们将测试分离算法，并观察其在标记传感器和不标记传感器的情况下的性能。对于需要在高噪音环境(如核磁共振扫描仪)中创建高保真、低噪音录音的研究人员，以及不是音频或生物电信号工程师的研究人员，建议的系统将很有用。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至中度听力损失患者的辅助听力设备，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，该系统将适合与生物电信号-脑电、肌电等-一起使用，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来源的位置。 与公共健康相关：我们建议开发一种系统来隔离和提取单独的信号源，无论是生物电信号(脑电、心电)还是声学信号源，以便能够开发其他与健康相关的应用程序。我们的系统的一个重要用途是作为轻度至中度听力损失患者的辅助听力设备，使他们能够在嘈杂的社交场合(如会议和餐馆)有效地发挥作用。对于需要在嘈杂环境中创建高保真、低噪声记录的研究人员来说，这也是有用的，比如核磁共振扫描仪。它同样适用于分离生物电信号，如胎儿和母亲的心跳，并提供每个来源的位置信息。

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.