Coordination Funds

协调基金

• 批准号: 318726059
• 负责人: Professor Dr.-Ing. Reinhold Häb-Umbach
• 金额: --
• 依托单位: Institut für Elektrotechnik und Informationstechnik
• 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 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/318726059?language=en
• 关键词: Coordination; Funds

In daily life, we are surrounded by a multitude of acoustic events, such as people speaking, the playback of music, and noises of all kinds. We are nevertheless able to effortlessly converse in such an environment, retrieve a desired voice while disregarding others, or draw conclusions about the composition of the environment and activities therein, given the observed sound scene. A technical system with similar capabilities would find numerous applications in fields as diverse as smart homes, ambient-assisted living, personal communications, and surveillance. With the continuously decreasing cost of acoustic sensors and the pervasiveness of wireless networks and mobile devices, the technological infrastructure for powerful wireless acoustic sensor networks is available, and the bottleneck for unleashing new disruptive applications is clearly on the algorithmic side.This Research Unit investigates solutions and limitations for acoustic signal processing and classification over acoustic sensor networks. We aim to tackle current shortcomings and to develop a common plattform which will make ASNs more adaptive to the variability of acoustic environments and sensor configurations, less dependent on supervision, and at the same time more trustworthy for the users. This will pave the way for a new class of applications that combine advanced acoustic signal processing with semantic analysis of audio. The project objectives will be achieved by adopting a three-layer approach treating communication and synchronization aspects on the lower layer, signal extraction and enhancement on the middle layer, and privacy-preserving acoustic scene classification and interpretation on the upper layer. To carry out those tasks we will blend advanced statistical signal processing with machine learning techniques. Our project will pioneer a generic, versatile framework for wireless acoustic sensor networks. This framework supports several classes of acoustic applications, both state-of-the-art and emerging ones.

在日常生活中，我们被各种各样的声音事件包围着，比如人们说话，音乐的回放，以及各种各样的噪音。然而，我们仍然能够在这样的环境中毫不费力地交谈，在忽略他人的情况下恢复所需的声音，或者根据观察到的声音场景得出关于环境的组成和其中的活动的结论。具有类似能力的技术系统将在智能家居、环境辅助生活、个人通信和监控等领域找到大量应用。随着声传感器成本的不断降低以及无线网络和移动设备的普及，强大的无线声传感器网络的技术基础设施是可用的，而释放新的颠覆性应用的瓶颈显然在算法方面。我们的目标是解决目前的不足，并开发一个通用平台，使ASN能够更好地适应声环境和传感器配置的变化，减少对监督的依赖，同时提高用户的可信度。这将为将高级声音信号处理与音频语义分析相结合的新型应用程序铺平道路。项目目标的实现将采用三层方法，即底层处理通信和同步方面，中间层处理信号提取和增强，上层处理保护隐私的声场分类和解译。为了执行这些任务，我们将把先进的统计信号处理与机器学习技术结合起来。我们的项目将开创一个通用的、通用的无线声传感器网络框架。该框架支持几类声学应用，既有最先进的，也有新兴的。