Near-Common Zeros in Blind Identification of Simo Acoustic Systems

The common zeros problem for Blind System Identification (BSI) has been well known to degrade the performance of classic BSI algorithms and therefore limits performance of subsequent speech dereverberation. Recently, we have shown that multichannel systems cannot be well identified if near-common zeros are present. In this work, we further study the near-common zeros problem using channel diversity measure. We then investigate the use of forced spectral diversity (FSD) based on a combination of spectral shaping filters and effective channel under modelling. Simulation results show the effectiveness of the proposed approach.

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