Performance Comparison of Combined Blind/Non-Blind Source Separation Algorithms
M. Joho, H Mathis
Abstract
M. Joho, H Mathis
Abstract
Source separation is becoming increasingly important in acoustical applications for spatial filtering. In the absence of any known source signals (blind case), a blind update equation similar to the natural gradient method [1] is presented, a derivative of which can be used in the case of known references (non-blind case). If some, but not all, source signals are known, blind-only algorithms are suboptimal, since some available information is not exploited. To overcome this problem, non-blind separation techniques can be incorporated. For the instantaneous mixing case (no time delays, no convolution), two different ways of combining blind and non-blind source separation methods are shown, namely an echo cancellertype and an equalizer-like approach. Simulations allow a comparison of the convergence time of both structures versus the convergence time of the blind-only case and clearly demonstrate the benefit of using combined blind/non-blind separation techniques. 1. INTRODUCTION Blind ...
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Source separation is becoming increasingly important in acoustical applications for spatial filtering. In the absence of any known source signals (blind case), a blind update equation similar to the natural gradient method [1] is presented, a derivative of which can be used in the case of known references (non-blind case). If some, but not all, source signals are known, blind-only algorithms are suboptimal, since some available information is not exploited. To overcome this problem, non-blind separation techniques can be incorporated. For the instantaneous mixing case (no time delays, no convolution), two different ways of combining blind and non-blind source separation methods are shown, namely an echo cancellertype and an equalizer-like approach. Simulations allow a comparison of the convergence time of both structures versus the convergence time of the blind-only case and clearly demonstrate the benefit of using combined blind/non-blind separation techniques. 1. INTRODUCTION Blind ...
Key concepts: Blind signal separation, Blind equalization, Algorithm, Convergence (economics), Source separation, Computer science, Independent component analysis, Convolution (computer science)