| Paper: | SS-6.4 | ||
| Session: | Convolutive Blind Source Separation for Speech and Audio Signals | ||
| Time: | Wednesday, May 19, 16:30 - 16:50 | ||
| Presentation: | Special Session Lecture | ||
| Topic: | Special Sessions: Convolutive Blind Source Separation for Speech and Audio Signals | ||
| Title: | TRINICON: A VERSATILE FRAMEWORK FOR MULTICHANNEL BLIND SIGNAL PROCESSING | ||
| Authors: | Herbert Buchner; Universität Erlangen-Nürnberg | ||
| Robert Aichner; Universität Erlangen-Nürnberg | |||
| Walter Kellermann; Universität Erlangen-Nürnberg | |||
| Abstract: | In this paper we present a framework for multichannel blind signal processing for convolutive mixtures, such as blind source separation (BSS) and multichannel blind deconvolution (MCBD). It is based on the use of multivariate pdfs and a compact matrix notation which considerably simplifies the representation and handling of the algorithms. By introducing these techniques into an information theoretic cost function, we can exploit the three fundamental signal properties nonwhiteness, nongaussianity, and nonstationarity. This results in a versatile tool that we call TRINICON ('Triple-N ICA for convolutive mixtures'). Both, links to popular algorithms and several novel algorithms follow from the general approach. In particular, we introduce a new concept of multichannel blind partial deconvolution (MCBPD) for speech which prevents a complete whitening of the output signals, i.e., the vocal tract is excluded from the equalization. This is especially interesting for automatic speech recognition applications. Moreover, we show results for BSS using multivariate spherically invariant random processes (SIRPs) to efficiently model speech, and show how the approach carries over to MCBPD. These concepts are also suitable for an efficient implementation in the frequency domain by using a rigorous broadband derivation avoiding the internal permutation problem and circularity effects. | ||
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