Signal Separation using Multichannel/Multimodal Side Information

使用多通道/多模式辅助信息进行信号分离

• 批准号: RGPIN-2014-03983
• 负责人: Dansereau, Richard
• 金额: $ 2.26万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=648638
• 关键词: Signal; Separation; using; Multichannel; Multimodal

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. Quite often, source separation is an inductive inference problem where the problem is highly underdetermined such that limited information exists for hoping to find a unique solution. Source separation problems may come with varying levels of information, including single channel mixtures, multichannel mixtures, or mixtures that are accompanied with side metadata that may help with the separation. For example, a music performance could be recorded by a single microphone (single channel), by multiple microphones distributed around the room (multichannel with spatial diversity), or access may be had to the musical score and instrument types (side metadata information).**The focus for this proposal is to research develop advanced source separation techniques that 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 when there are lack of time alignment information wit the metadata, small variations in time-frequency or sparsity information between the mixtures and the metadata, effects of short-time non-stationarities in the source signals being separated, and use of sparsity constraints in multiple domains.**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.

信号分离的目的是从一个或多个信号混合中恢复源信号。这些混合信号可以来自多个领域，包括用于所谓鸡尾酒会问题的语音混合、多种乐器和/或人声的音乐录音、来自大脑电活动的脑电图仪(EEG)信号、用于胎儿监护的测量表面电位时的母体/胎儿心电图仪(ECG)混合、来自肌肉运动单位动作电位的混合的肌电(EMG)信号、具有弱天文信号源与背景的混合的各种天文数据集、以及雷达或声纳系统。通常，源分离是一个归纳推理问题，其中问题的确定程度很低，因此存在有限的信息，希望找到唯一的解决方案。源分离问题可能伴随着不同级别的信息，包括单通道混合、多通道混合或伴随可能有助于分离的边元数据的混合。例如，音乐表演可以由单个麦克风(单通道)录制，可以由分布在房间周围的多个麦克风(多通道空间多样性)录制，或者可以访问乐谱和乐器类型(侧元数据信息)。**本提案的重点是研究开发高级源分离技术，将多通道和多模元数据作为边信息来提高源分离的质量。信号处理研究将集中于将多通道和辅助信息合并到源分离问题中，并评估这种辅助元数据信息的影响。特别令人感兴趣的是当元数据缺乏时间对准信息时，混合和元数据之间的时频或稀疏信息的微小变化，被分离的源信号中的短时非平稳性的影响，以及在多个域中使用稀疏约束。**这些信号分离特征将在包括语音、人声/乐器分离和EMG信号分解的各种应用下被研究和研究，但是，关键的研究重点将是具有侧元数据信息的多通道混合中源分离的核心成分。一些预期的结果如下。首先，在声乐/乐器分离空间中，我们期望能够获得音乐记录，很可能是立体声多声道记录，以及可以包括乐谱、演奏中的乐器的声音样本、第二音乐家的作品的示例演奏、歌词列表、MIDI文件或其他类型的元数据的辅助元数据，并且能够分离记录中的单独的乐器以隔离或抑制单独的乐器。在肌电信号分解空间中，我们希望能够进行多通道肌电记录，并确定肌肉中单个运动单位的放电时间和相应的运动单位动作电位的形状。在胎儿心电信号分离空间中，我们期望能够采取包含较强的母体心电信号和较弱的胎儿心电信号的多通道混合，并能够以一种改进的方式抑制较强的母体心电信号。拟议的信号分离核心研究将具有更广泛的适用性，这些例子将用于验证这些技术，并确定可能在一个领域中出现而不是在另一个领域中出现的其他限制。