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Independent Vector Analysis is a powerful tool for estimating the broadband acoustic transfer function between multiple sources and the microphones in the frequency domain. In this work, we consider an extended IVA model which adopts the concept of pilot dependent signals. Without imposing any constraint on the de-mixing system, pilot signals depending on the target source are injected into the model...
Unknown global permutation of the separated sources, time-varying source activity and under determination are common problems affecting on-line Independent Vector Analysis when applied to real-world speech enhancement. In this work we propose to extend the signal model of IVA by introducing additional supervising components. Pilot signals, which are dependent on the sources, are injected in the multidimensional...
In this article we propose a computationally efficient method (termed FCOMBI) to combine the strengths of non-Gaussianity-based Blind Source Separation (BSS) and cross-correlations-based BSS. This is done by fusing the separation abilities of two well-known BSS algorithms: EFICA and WASOBI. Simulations show that our approach is at least as accurate and often more accurate that other state-of-the-art...
In signal enhancement applications, a reference signal which provides information about interferences and noise is desired. It can be obtained via a multichannel filter that performs a spatial null in the target position, a so-called target-cancelation filter. The filter must adapt to the target position, which is difficult when noise is active. When the target location is confined to a small area,...
This paper summarizes the 2013 community-based Signal Separation Evaluation Campaign (SiSEC 2013). Five speech and music datasets were contributed, including two new datasets: “Two-channel noisy recordings of a moving speaker within a limited area” and “Asynchronous recordings of speech mixtures”. The participants addressed one or more datasets out of five, and the results for each task were evaluated...
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