Coordination Funds

• Applicant: Professor Dr.-Ing. Reinhold Häb-Umbach
• Subject Area: Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering ; Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2016 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 282835863

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.

• DFG Programme: Research Units
• Subproject of: FOR 2457: Acoustic Sensor Networks