Signal Separation and Enhancement using Multichannel/Multimodal Side Information

信号分离的目的是从一个或多个信号混合物中恢复源信号。这些混合物可以来自多个领域，包括用于所谓鸡尾酒会问题的语音混合物、多种乐器和/或人声的音乐记录、来自大脑电活动的脑电图（EEG）信号、测量胎儿监测表面电位时的母体/胎儿心电图（ECG）混合物、来自肌肉运动单位动作电位混合物的肌电信号（EMG）信号各种天文数据集与弱天文源与背景，雷达或声纳系统的混合物。本建议的重点是研究和开发先进的源分离技术，使用多通道和多模态元数据作为辅助信息，以提高源分离的质量。信号处理研究将集中于将多通道和辅助信息纳入源分离问题，并评估此类辅助元数据信息的影响。特别令人感兴趣的是扩展非负矩阵/张量因子分解技术，并通过机器学习和深度学习（最初通过卷积神经网络）结合边信息和收敛约束。这些信号分离特征将在各种应用中进行研究和研究，包括语音、人声/乐器分离和EMG信号分解，但重点研究将集中在具有辅助元数据信息的多通道混合中源分离的核心组件上。一些预期成果如下。首先，在声乐/乐器分离空间中，我们期望能够将音乐记录（可能作为立体声多声道记录）沿着有辅助元数据，其可以包括乐谱、表演中乐器的声音样本、次要音乐家对作品的示例表演、歌词列表、录音文件或其他类型的元数据，并且能够分离记录中的各个乐器以隔离或抑制各个乐器。在EMG信号分解空间中，我们期望能够进行多通道EMG记录，并确定肌肉中各个运动单元的放电时间以及运动单元动作电位的相应形状。在胎儿ECG分离空间中，我们期望能够采用包含较强的母亲ECG和较弱的胎儿ECG的多通道混合，并且能够以改进的方式抑制较强的母亲ECG信号。拟议的信号分离核心研究将具有更广泛的适用性，这些示例将用于验证技术，并确定可能在一个领域而不是另一个领域出现的其他限制。

The aim of signal separation is to recover source signals from one or more signal mixtures. These mixtures can come from a multitude of domains, including speech mixtures for the so-called cocktail party problem, musical recordings of multiple instruments and/or vocals, electroencephalograph (EEG) signals from the electrical activity of the brain, maternal/fetal electrocardiograph (ECG) mixtures when measuring surface potentials for fetal monitoring, electromyography (EMG) signals from mixtures of motor unit action potentials in muscles, various astronomical datasets with mixtures of weak astronomical sources versus background, and radar or sonar systems. The focus for this proposal is to research and develop advanced source separation techniques using multichannel and multimodal metadata as side information to improve the quality in source separation. The signal processing research will concentrate on incorporating multichannel and side information into the source separation problem and in evaluating the impact of such side metadata information. Of particular interest is to extend non-negative matrix/tensor factorization techniques and incorporate side-information and convergence constraints through machine learning and deep learning, initially via convolutional neural networks. These signal separation characteristics will be research and studied under various applications, including speech, vocals/instrument separation, and EMG signal decomposition, but, the key research focus will be on the core components of source separation in multichannel mixtures with side metadata information. Some of the anticipated outcomes are as follows. First, in the vocal/instrument separation space, we expect to be able to take a musical recording, likely as a stereo multichannel recording, along with side metadata, which could include the musical score, sound samples of the instruments in the performance, an example performance of the piece by a secondary musician, a list of lyrics, a MIDI file, or other types of metadata, and be able to separate the individual instruments in the recording to either isolate or suppress individual instruments. In the EMG signal decomposition space, we expect to be able to take multichannel EMG recordings and determine firing times of individual motor units in the muscle and the corresponding shape of the motor unit action potentials. In the fetal ECG separation space, we expect to be able take a multichannel mixtures containing the stronger maternal ECG and weaker fetal ECG, and be able to suppress the stronger maternal ECG signal in an improved fashion. The proposed core research in signal separation will have wider applicability, where these examples would be used to validate the techniques and determine other limitations that may present themselves in one domain but not another.