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Relative transfer function estimation exploiting instantaneous signals and the signal subspace

: Taseska, Maja; Habets, Emanuël A.P.


European Association for Signal Processing -EURASIP-; Institute of Electrical and Electronics Engineers -IEEE-:
23rd European Signal Processing Conference, EUSIPCO 2015 : August 31 - September 4, 2015, Nice
Piscataway, NJ: IEEE, 2015
ISBN: 978-0-9928626-3-3
ISBN: 978-0-9928626-4-0
European Signal Processing Conference (EUSIPCO) <23, 2015, Nice>
Fraunhofer IIS ()

Multichannel noise reduction can be achieved without distorting the desired signals, provided that the relative transfer functions (RTFs) of the sources are known. Many RTF estimators require periods where only one source is active, which limits their applicability in practice. We propose an RTF estimator that does not require such periods. A time-varying RTF is computed per time-frequency (TF) bin that corresponds to the dominant source at that bin. We demonstrate that a minimum variance distortionless response (MVDR) filter based on the proposed RTF estimate can extract multiple sources with low distortion. The MVDR filter has maximum degrees of freedom and hence achieves significantly better noise reduction compared to a linearly constrainedminimumvariance filter that uses a separate RTF for each source.