  This study describes an online target sound extraction (TSE) process, derived
from the iterative batch algorithm using the similarity-and-independence-aware
beamformer (SIBF), to achieve both latency reduction and extraction accuracy
maintenance. The SIBF is a linear method that estimates the target more
accurately compared with a reference, an approximate magnitude spectrogram of
the target. Evidently, deriving the online algorithm from the iterative batch
algorithm reduces the latency of the SIBF; however, this process presents two
challenges: 1) the derivation may degrade the accuracy, and 2) the conventional
post-process, meant for scaling the estimated target, may increase the accuracy
gap between the two algorithms. To maintain the best possible accuracy, herein,
an approach that minimizes this gap during post-processing is adopted, and a
novel scaling method based on the single-channel Wiener filter (SWF-based
scaling) is proposed. To improve the accuracy further, the
time-frequency-varying variance generalized Gaussian (TV GG) distribution is
employed as a source model to represent the joint probability between the
target and reference. Thus, experiments using the CHiME-3 dataset confirm that
1) the online algorithm reduces latency; 2) SWF-based scaling eliminates the
gap between the two algorithms while improving the accuracy; 3) TV GG model
achieves the best accuracy when it corresponds to the Laplacian model; and 4)
our online SIBF outperforms the conventional linear TSE, including the minimum
mean square error beamformer. These findings can contribute to the fields of
beamforming and blind source separation.
