Acoustic Signal Extraction and Enhancement

声学信号提取和增强

• 批准号: 318506776
• 负责人: Professor Dr.-Ing. Walter Kellermann
• 金额: --
• 依托单位: Lehrstuhl für Multimediakommunikation und Signalverarbeitung
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/318506776?language=en
• 关键词: Acoustic; Signal; Extraction; Enhancement

This project is dedicated to advancing intelligent algorithms for signal extraction and enhancement using acoustic sensor networks (ASNs). Representing Layer 2 in the three-layered approach followed by the proposed research unit, it is based on the ASN infrastructure provided by Layer 1, and delivers enhanced signals of possibly multiple target sources of interest to Layer 3 to allow acoustic scene analysis and understanding. As a core component serving the entire research unit, this project establishes and augments a so-called acoustic map which represents the current state and the dynamics of the acoustic scenario. As basic information, this acoustic map includes attributes of sensors, sources and the acoustic environment, from which higher-level information is derived, e.g., the utility of a given sensor node for a specific task related to a certain source. The entries in the acoustic map are a result of parameter estimation, signal processing, and data-driven learning algorithms and also incorporate prior knowledge. They will be represented by a probabilistic framework and thus include reliability information, which is then heavily exploited for the tasks of signal extraction and enhancement in this project and all other projects. Beyond the blind and semi-blind spatiotemporal filtering algorithms of the preceding project phase, the use of additional reference information for signal enhancement is emphasized in the proposed project. First, acoustic echo cancellation (AEC) will be generalized to the multiple-input/multiple-output (MIMO) case as given by multiple loudspeakers and multiple microphones in the ASN scenario. Here, the acoustic map information will determine for which of the individual loudspeaker-enclosure-microphone paths the classical AEC paradigm of supervised multichannel adaptive filtering is applicable and useful. For other sources of interference, reference information, such as location, activity patterns or spatial and spatiotemporal features, will be estimated or learned by the most useful sensors and shared via the acoustic map so that it can be optimally used throughout the ASN. Adding to the potential of distributed sensing, mobile sensor nodes, e.g., attached to robots, allow exploration and improved coverage of dynamic scenarios. The resulting time-varying sensor array topologies will be optimized for source localization and signal enhancement by selecting optimum subsets of fixed sensors and by optimizing a robot’s trajectories. Accounting for the distributed sensing and distributed processing capacity of the network infrastructure provided by P1, distributed algorithms for the acquisition and fusion of acoustic map information and for efficient AEC and signal enhancement will be designed. To verify their performance in realistic scenarios, selected novel distributed algorithms will be incorporated into a real-time demonstrator that will be jointly developed by all projects.

该项目致力于推进使用声学传感器网络（ASN）进行信号提取和增强的智能算法。代表第2层的三层的方法，其次是拟议的研究单位，它是基于第1层提供的可识别基础设施，并提供可能多个目标源的增强信号层3，使声学场景分析和理解。 作为服务于整个研究单元的核心组件，该项目建立并增强了一个所谓的声学地图，它代表了声学场景的当前状态和动态。 作为基本信息，该声学地图包括传感器、源和声环境的属性，从这些属性导出更高级别的信息，例如，给定传感器节点用于与特定源相关的特定任务的效用。 声学图中的条目是参数估计、信号处理和数据驱动学习算法的结果，并且还包含先验知识。它们将由概率框架表示，因此包括可靠性信息，然后在本项目和所有其他项目中大量利用这些信息进行信号提取和增强。除了前面项目阶段的盲和半盲空时滤波算法之外，在所提出的项目中强调使用额外的参考信息进行信号增强。首先，声学回声消除（AEC）将被推广到多输入/多输出（MIMO）的情况下，由多个扬声器和多个麦克风在MIMO场景。这里，声学映射信息将确定监督多通道自适应滤波的经典AEC范例对于各个扬声器-封闭式扬声器-麦克风路径中的哪一个是适用的和有用的。 对于其他干扰源，参考信息，如位置，活动模式或空间和时空特征，将由最有用的传感器估计或学习，并通过声学地图共享，以便它可以最佳地使用整个传感器。增加了分布式感测的潜力，移动的传感器节点，例如，附加到机器人上，允许探索和改进动态场景的覆盖范围。由此产生的随时间变化的传感器阵列拓扑结构将被优化的源定位和信号增强，通过选择最佳子集的固定传感器，并通过优化机器人的轨迹。考虑到P1提供的网络基础设施的分布式传感和分布式处理能力，将设计用于声学地图信息的获取和融合以及用于高效AEC和信号增强的分布式算法。为了验证它们在现实场景中的性能，选定的新型分布式算法将被纳入一个实时演示器，该演示器将由所有项目共同开发。