Acoustic Signal Extraction and Enhancement

该项目致力于利用声学传感器网络（ASNs）推进信号提取和增强的智能算法。它以Layer 1提供的ASN基础设施为基础，代表三层方法中的Layer 2，并向Layer 3提供可能感兴趣的多个目标源的增强信号，以允许声学场景分析和理解。作为服务于整个研究单位的核心组件，该项目建立并增强了所谓的声学地图，它代表了声学场景的当前状态和动态。作为基本信息，该声学图包括传感器、声源和声环境的属性，并从中衍生出更高层次的信息，例如，给定传感器节点对与某个声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，也包含了先验知识。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信号提取和增强任务。除了前一个项目阶段的盲和半盲时空滤波算法之外，该项目还强调使用额外的参考信息来增强信号。首先，声学回声消除（AEC）将推广到ASN场景中由多个扬声器和多个麦克风给出的多输入/多输出（MIMO）情况。在这里，声学地图信息将决定在单个扬声器-机箱-麦克风路径中，经典的监督多通道自适应滤波AEC范式是适用的和有用的。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，以便在整个ASN中得到最佳使用。增加了分布式传感的潜力，移动传感器节点，例如，连接到机器人，允许探索和改进动态场景的覆盖范围。所得到的时变传感器阵列拓扑将通过选择固定传感器的最佳子集和优化机器人的轨迹来优化源定位和信号增强。考虑到P1提供的网络基础设施的分布式感知和分布式处理能力，将设计用于声学地图信息采集和融合以及高效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由所有项目共同开发的实时演示器中。

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.