  Multichannel convolutive blind speech source separation refers to the problem
of separating different speech sources from the observed multichannel mixtures
without much a priori information about the mixing system. Multichannel
nonnegative matrix factorization (MNMF) has been proven to be one of the most
powerful separation frameworks and the representative algorithms such as MNMF
and the independent low-rank matrix analysis (ILRMA) have demonstrated great
performance. However, the sparseness properties of speech source signals are
not fully taken into account in such a framework. It is well known that speech
signals are sparse in nature, which is considered in this work to improve the
separation performance. Specifically, we utilize the Bingham and Laplace
distributions to formulate a disjoint constraint regularizer, which is
subsequently incorporated into both MNMF and ILRMA. We then derive
majorization-minimization rules for updating parameters related to the source
model, resulting in the development of two enhanced algorithms: s-MNMF and
s-ILRMA. Comprehensive simulations are conducted, and the results unequivocally
demonstrate the efficacy of our proposed methodologies.
