A Survey of Convolutive Blind Source Separation Methods
|Michael Syskind Pedersen, Jan Larsen, Ulrik Kjems, Lucas C. Parra|
|Abstract||During the past decades, much attention has been|
given to the separation of mixed sources, in particular
for the blind case where both the sources and
the mixing process are unknown and only recordings
of the mixtures are available. In several situations it
is desirable to recover all sources from the recorded
mixtures, or at least to segregate a particular source.
Furthermore, it may be useful to identify the mixing
process itself to reveal information about the physical
In some simple mixing models each recording
consists of a sum of differently weighted source signals.
However, in many real-world applications, such
as in acoustics, the mixing process is more complex.
In such systems, the mixtures are weighted and delayed,
and each source contributes to the sum with
multiple delays corresponding to the multiple paths
by which an acoustic signal propagates to a microphone.
Such filtered sums of different sources are
called convolutive mixtures.
There are already a number of partial reviews
available on this topic so the purpose of this chapter is to provide
a complete survey of convolutive BSS and identify
a taxonomy that can organize the large number
of available algorithms. This may help practitioners
and researchers new to the area of convolutive source
separation obtain a complete overview of the field.
Hopefully those with more experience in the field can
identify useful tools, or find inspiration for new algorithms.
|Keywords||blind source separation, ica, convolutive|
|Book title||Springer Handbook of Speech Processing|
|Editors||Jacob Benesty, Yiteng Huang, Mohan Sondhi|
|Year||2007 Month November|
|ISBN / ISSN||ISBN 978-3-540-49125-5|
|BibTeX data|| [bibtex]|
|IMM Group(s)||Intelligent Signal Processing|