Marco Kühne

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—Successful localization of sound sources in rever-berant enclosures is an important prerequisite for many spatial signal processing algorithms. We investigate the use of a weighted fuzzy c-means cluster algorithm for robust source localization using location cues extracted from a microphone array. In order to increase the algorithm's robustness against(More)
This paper investigates the use of DUET, a recently proposed blind source separation method, as front-end for missing data speech recognition. Based on the attenuation and delay estimation in stereo signals soft time-frequency masks are designed to extract a target speaker from a mixture containing multiple speech sources. A postprocessing step is(More)
This paper presents a novel approach to combine microphone array processing and robust speech recognition for reverberant multi-speaker environments. Spatial cues are extracted from a microphone array and automatically clustered to estimate local-ization masks in the time-frequency domain. The localization masks are then used to blindly design adaptive(More)