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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. A Bayesian Approach to Informed Spatial Filtering with Robustness Against DOA Estimation Errors
 IEEE ACM transactions on audio, speech, and language processing 26 (2018), Nr.1, S.145160 ISSN: 23299290 ISSN: 23299304 

 Englisch 
 Zeitschriftenaufsatz 
 Fraunhofer IIS () 
Abstract
A Bayesian approach to spatial filtering is presented, which is robust to uncertain or erroneous directionofarrival (DOA) information. The proposed framework aims to capture multiple sound sources at each timefrequency instant with an arbitrary directiondependent gain, while attenuating diffuse sound and noise. For robustness, the DOA corresponding to each sound source is assumed to be a discrete random variable with a prior defined on a discrete set of candidate DOAs over the whole DOA space. With this assumption, the desired spatial filter is given as a weighted sum of spatial filters corresponding to a specific combination of probable DOA values, where the weights are given by the joint posterior probabilities of the combination of DOA values. Assuming the whole DOA space as the support for each random variable results in redundant computations and contributes to a high computational cost. To alleviate this problem, a narrowband DOA estimatebased posterior proba bility approximation method is proposed, which isolates regions in the DOA space with high probability of containing the actual source DOAs to compute timeadaptive supports for each random variable. Through experimental analysis, we demonstrate the robustness of the proposed framework againstDOAestimation errors. Experimental evaluation with simulated and measured room impulse responses, in terms of objective performance measures, demonstrates the effectiveness of the framework to perform spatial filtering in noisy and reverberant acoustic environments.