Speech Dereverberation by Blind Adaptive MIMO Filtering Exploiting Nongaussianity, Nonwhiteness, and Nonstationarity
Conference: Sprachkommunikation 2010 - 9. ITG-Fachtagung
10/06/2010 - 10/08/2010 at Bochum, Deutschland
Proceedings: Sprachkommunikation 2010
Pages: 4Language: englishTyp: PDFPersonal VDE Members are entitled to a 10% discount on this title
Buchner, Herbert (Deutsche Telekom Laboratories, Berlin University of Technology, Ernst-Reuter-Platz 7, 10587 Berlin, Germany)
Kellermann, Walter (Multimedia Communications and Signal Processing, University of Erlangen-Nuremberg, Cauerstr. 7, 91058 Erlangen, Germany)
In this paper, we present a class of novel algorithms for blind dereverberation of speech signals based on TRINICON, a general framework for broadband adaptive MIMO signal processing. In order to exploit all fundamental stochastic signal properties of speech for the dereverberation/deconvolution process and to avoid any whitening artifacts known from previous approaches, we propose the incorporation of a specially designed signal model based on an expansion using multivariate Chebyshev-Hermite polynomials. The multivariate model also inherently includes linear prediction which is known to be related directly to the human vocal tract model. The framework is applicable to both single-speaker scenarios and also to multiple simultaneously active speakers. In the latter case it also includes blind source separation in addition to the dereverberation.