  Convolutive source separation is often done in two stages: 1) estimation of
the mixing filters and 2) estimation of the sources. Traditional approaches
suffer from the ambiguities of arbitrary permutations and scaling in each
frequency bin of the estimated filters and/or the sources, and they are usually
corrected by taking into account some special properties of the
filters/sources. This paper focusses on the filter permutation problem in the
absence of scaling, investigating the possible use of the temporal sparsity of
the filters as a property enabling permutation correction. Theoretical and
experimental results highlight the potential as well as the limits of sparsity
as an hypothesis to obtain a well-posed permutation problem.

Combinatorics