SPATIALLY INFORMED INDEPENDENT VECTOR ANALYSIS FOR SOURCE EXTRACTION BASED ON THE CONVOLUTIVE TRANSFER FUNCTION MODEL
Xianrui Wang (Northwestern Polytechnical University); Andreas Brendel ( Friedrich-Alexander-University Erlangen-Nürnberg); Gongping Huang (University of Erlangen-Nuremberg); Yichen Yang ( Northwestern Polytechnical University); Walter Kellermann (Friedrich-Alexander-University Erlangen-Nürnberg); Jingdong Chen (Northwestern Polytechnical University)
-
SPS
IEEE Members: $11.00
Non-members: $15.00
Spatial information can help improve source separation performance.
Numerous spatially informed source extraction methods based
on the independent vector analysis (IVA) have been developed,
which can achieve reasonably good performance in non- or weakly
reverberant environments. However, the performance of those
methods degrades quickly as the reverberation increases. The
underlying reason is that those methods are derived based on the
multiplicative transfer function model with a rank-1 assumption,
which does not hold true if reverberation is strong. To circumvent
this issue, this paper proposes to use the convolutive transfer
function (CTF) model to improve the source extraction performance
and develop a spatially informed IVA algorithm. Simulations
demonstrate the efficacy of the developed method even in highly
reverberant environments.