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Spectrogram of acoustic scene Bildinformationen anzeigen

Spectrogram of acoustic scene

Welcome to DFG FOR 2457 Acoustic Sensor Networks

In daily life, we are surrounded by a multitude of noises and other acoustic events. Nevertheless we are 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 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 of wireless acoustic sensor networks is available, and the bottleneck for unleashing new applications is clearly on the algorithmic side.

This Research Unit aims at rendering acoustic signal processing and classification over acoustic sensor networks more 'intelligent', 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 array of applications which 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, signal extraction and enhancement on the middle, and acoustic scene classification and interpretation on the upper layer. We aim at applying a consistent methodology for optimization across these layers and a unification of advanced statistical signal processing with Bayesian learning and other machine learning techniques. Thus, the project is dedicated to pioneering work towards a generic and versatile framework for the integration of acoustic sensor networks in several classes of state-of-the-art and emerging applications.

Research goals

This RU is dedicated to address the key scientific challenges of next-generation audio processing, based on acoustic sensor networks. It will not restrict itself to a single application but rather consider the challenges common to many applications. The goals are summarized as follows:

  • Communication and audio processing over acoustic sensor networks:  Signal processing algorithms will be developed that are aware of the limitations of the communication network and processing units and that strike an optimal balance between centralized and distributed processing. Further, the communication protocol will be made aware of the signal processing needs to optimally organize data streams and allocate resources, trading off energy efficiency, communication bandwidth requirements, and signal extraction and enhancement performance.
  • Time synchronization of distributed sensor nodes: We will develop waveform-based sampling clock synchronization algorithms that derive estimates of the sampling rate offsets from the observed acoustic signals. As an alternative for challenging acoustic environments we will achieve clock synchronization by exchanging time stamps over the wireless communication link.
  • Acoustic signal extraction and enhancement from natural acoustic environments: Novel concepts for semi-blind signal extraction and enhancement will be developed that combine the generic potential of blind, especially ICA-based algorithms to extract signals from unspecified scenes with the efficiency and robustness of supervised algorithms for spatiotemporal filtering. The estimated information rendering the blind signal extraction algorithms "informed" will be represented and processed -- as much as possible and reasonable -- under the paradigm of Bayesian learning, such that it can be easily exchanged with the other tasks addressed in the RU.
  • Learning and classification of acoustic events and scenes: We will develop unsupervised learning algorithms for acoustic event detection and scene classification to cater for a broad range of signals and absence of labeled training data. The issue of overlapping sounds will be approached from a signal processing perspective employing the aforementioned advanced signal extraction algorithms. Unsupervised feature learning techniques will be employed to find appropriate representations for a broad range of possible audio signals.
  • Ensuring privacy: While we may assume that for the local exchange of audio data between adjacent nodes encryption schemes and protocols are available, the diffusion of data across larger
    areas or into the cloud may pose more serious privacy concerns. Therefore, in this RU we investigate an approach that is based on the data-minimisation paradigm. We will investigate audio features that provide a scalable amount of information with respect to temporal, spectral, and spatial dimensions. While they should carry sufficient information for the signal analysis or classification task at hand, they will be tuned to be less informative about assumed private aspects. For example, suprasegmental features, which will be employed to classify an acoustic scene, will be designed to not allow the reconstruction of speech. Obviously, there is a tradeoff involved which constitutes the central part of our research question.