TRINICON for Dereverberation of Speech and Audio Signals

In this chapter, we develop an analytical top-down approach to the problem of blind dereverberation of speech and audio signals based on TRINICON (TRIple-N Independent component analysis for CONvolutive mixtures), a general framework for broadband adaptive Multi-Input Multi-Output (MIMO) signal processing. Two fundamentally different approaches to the dereverberation problem for realistic scenarios can be distinguished: The “identification-and-inversion approach”, which results in a two-step procedure consisting of blind identification of the acoustic MIMO mixing system, followed by an inversion of the identified system. As an alternative, the “direct-inverse approach” blindly estimates the inverse of the acoustic mixing system directly. As shown in this chapter, for both cases TRINICON yields the information-theoretically optimum estimation procedures in a unified way and allows for a direct comparison between the approaches, paves the way to synergies, and yields various useful insights for practical realizations. This chapter also relates other known algorithms, and presents novel improved algorithms as special cases of the generic concept.

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