  Target source extraction is significant for improving human speech
intelligibility and the speech recognition performance of computers. This study
describes a method for target source extraction, called the
similarity-and-independence-aware beamformer (SIBF). The SIBF extracts the
target source using a rough magnitude spectrogram as the reference signal. The
advantage of the SIBF is that it can obtain a more accurate signal than the
spectrogram generated by target-enhancing methods such as speech enhancement
based on deep neural networks. For the extraction, we extend the framework of
deflationary independent component analysis (ICA) by considering the
similarities between the reference and extracted target sources, in addition to
the mutual independence of all the potential sources. To solve the extraction
problem by maximum-likelihood estimation, we introduce three source models that
can reflect the similarities. The major contributions of this study are as
follows. First, the extraction performance is improved using two methods,
namely boost start for faster convergence and iterative casting for generating
a more accurate reference. The effectiveness of these methods is verified
through experiments using the CHiME3 dataset. Second, a concept of a fixed
point pertaining to accuracy is developed. This concept facilitates
understanding the relationship between the reference and SIBF output in terms
of accuracy. Third, a unified formulation of the SIBF and mask-based beamformer
is realized to apply the expertise of conventional BFs to the SIBF. The
findings of this study can also improve the performance of the SIBF and promote
research on ICA and conventional beamformers.
  Index Terms: beamformer, independent component analysis, source separation,
speech enhancement, target source extraction
