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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587533
• 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，包括用于所谓的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、当测量用于胎儿监测的表面电位时的母体/胎儿心电图（ECG）混合物、来自肌肉中的运动单位动作电位的混合物的肌电图（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。很多时候，源分离是一个归纳推理问题，其中问题是高度欠定的，这样就存在有限的信息，希望找到一个唯一的解决方案。源分离问题可能伴随着不同级别的信息，包括单声道混合、多声道混合或伴随有可能有助于分离的辅助元数据的混合。例如，音乐表演可以由单个麦克风（单通道）、由分布在房间周围的多个麦克风（具有空间多样性的多通道）记录，或者可以访问乐谱和乐器类型（侧元数据信息）。 本文的重点是研究开发先进的源分离技术，以多通道和多模态元数据作为辅助信息，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。特别感兴趣的是，当缺乏时间对齐信息与元数据，时间-频率或稀疏信息之间的混合物和元数据的小的变化，被分离的源信号中的短时间非平稳性的影响，以及在多个域中的稀疏约束的使用。 这些信号分离特性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，但是，重点研究将集中在多通道混合物中的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。