Björn Schölling

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Most algorithms based on Computational Auditory Scene Analysis (CASA) for binaural speech separation do not have the ability to inhibit already localized and for a long time present sources in the auditory scene. This has the major drawback that the auditory cues of weaker and new sources are subject to interference from already localized and perceived(More)
We present a new way of modelling the Precedence Effect to enable the robust measurement of localization cues (ITD and IID) in echoic environments. Based on this we developed a localization system which is inspired by the auditory system of mammals. It uses a Gammatone filter bank for preprocessing and extracts the ITD cue via zero crossings (IID(More)
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