Coordination Funds

在日常生活中，我们被大量的声学事件所包围，例如人们说话，音乐播放和各种噪音。尽管如此，我们仍然能够毫不费力地在这样的环境中匡威，在忽略其他声音的情况下检索所需的声音，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。具有类似功能的技术系统将在智能家居、环境辅助生活、个人通信和监控等不同领域找到许多应用。随着声学传感器成本的不断降低以及无线网络和移动的设备的普及，强大的无线声学传感器网络的技术基础设施是可用的，而释放新的破坏性应用的瓶颈显然是在算法方面。我们的目标是解决目前的缺点，并开发一个共同的平台，这将使ASNs更适应的声学环境和传感器配置的变化，较少依赖于监督，并在同一时间更值得信赖的用户。这将为将联合收割机高级声学信号处理与音频语义分析相结合的新一类应用铺平道路。我们会采用三层方法，在低层处理通讯和同步事宜，在中层处理信号提取和增强工作，在上层处理保障隐私的声景分类和解译工作，以达致项目目的。为了完成这些任务，我们将把先进的统计信号处理与机器学习技术相结合。我们的项目将开创一个通用的，多功能的无线声学传感器网络的框架。该框架支持几类声学应用，包括最先进的和新兴的应用。

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.