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中恢复源信号。这些混合可以来自许多领域，包括所谓的鸡尾酒会问题的语音混合，多种乐器和/或声乐的音乐记录，来自大脑电活动的脑电图（EEG）信号，用于胎儿监测测量表面电位时的母体/胎儿心电图（ECG）混合，来自肌肉运动单位动作电位混合的肌电图（EMG）信号，各种天文数据集，包括弱天文源与背景的混合，以及雷达或声纳系统。本课题的重点是研究和开发利用多通道和多模态元数据作为侧信息的先进信源分离技术，以提高信源分离的质量。信号处理的研究将集中于将多通道和侧元信息纳入到信源分离问题中，并评估此类侧元信息的影响。特别感兴趣的是扩展非负矩阵/张量分解技术，并通过机器学习和深度学习结合侧信息和收敛约束，最初通过卷积神经网络。这些信号分离特性将在各种应用下进行研究，包括语音、人声/乐器分离和肌电信号分解，但是，重点研究将集中在具有侧元数据信息的多通道混合源分离的核心组成部分。一些预期的结果如下。首先，在人声/乐器分离空间中，我们希望能够采用音乐录音，可能是立体声多声道录音，以及附带的元数据，其中可能包括乐谱、演奏中乐器的声音样本、副音乐家的作品表演示例、歌词列表、MIDI文件或其他类型的元数据。并且能够在录音中分离单个乐器来隔离或抑制单个乐器。在肌电图信号分解空间中，我们期望能够进行多通道肌电图记录，并确定肌肉中单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。在胎儿心电分离空间中，我们期望能够采用包含较强的母体心电和较弱的胎儿心电的多通道混合，并能够以改进的方式抑制较强的母体心电信号。建议的信号分离核心研究将具有更广泛的适用性，这些例子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.