Estimating multiple filters from stereo mixtures: a double sparsity approach Simon Arberet

Prasad Sudhakar and Rémi Gribonval

EPFL [email protected]

INRIA Rennes - Bretagne Atlantique {firstname.lastname}@inria.fr

Abstract—We consider the problem of estimating multiple filters from convolutive mixtures of several unknown sources. We propose to exploit both the time-frequency (TF) sparsity of the sources and the sparsity of the mixing filters. Our framework consists of: a) a clustering step to group the TF points where only one source is active, for each source; b) a convex optimisation step, to estimate the filters using TF cross-relations that capture linear constraints satisfied by the unknown filters. Experiments demonstrate that the approach is well suited for the estimation of sufficiently sparse filters.

I. I NTRODUCTION AND NOTATIONS P Given two convolutive mixtures xi = N j=1 aij ? sj , i = 1, 2, we wish to estimate the mixing filters aij from the mixtures without the knowledge of the sources sj . II. C ROSS - RELATIONS FOR BLIND FILTER ESTIMATION In the single source setting and in the absence of noise, the so-called time-domain cross-relation holds. A traditional method to solve for the filters using it is to minimise kx2 ? a1 − x1 ? a2 k2 with a normalisation constraint on the filters [1] (as there is only one source, the source index is dropped on the filters). Denoting B := B[x1 , x2 ] a matrix built by concatenating Toeplitz matrices derived from the observed mixtures, this leads to the minimisation of kB·ak2 subject to kak2 = 1 where a is a concatenation of the vectorized unknown filters. The normalisation kak2 = 1 is to avoid the trivial zero-vector solution. It can be replaced by kak1 = 1 to seek sparse filters [2]. However, these approaches are non-convex and suffer from a shift ambiguity of the solution. Instead, we propose the following convex optimisation problem min kak1 s.t. kB · ak2 ≤  and a1 (t0 ) = 1 a

(1)

where t0 is an arbitrarily chosen time index. We show that the new problem no longer suffers from a shift ambiguity. This work was supported in part by the French Agence Nationale de la Recherche (ANR), project ECHANGE (ANR-08EMER-006) and by the EU FET-Open project FP7-ICT-225913SMALL.

III. M ULTIPLE SPARSE FILTER ESTIMATION In the presence of multiple sources, the time-domain cross-relation does not hold anymore. We extend the cross-relation approach to multiple sources, assuming that: the sources are sparse in the TF domain; we know large enough TF regions where each source is the only one contributing to the mixtures. Cross-relations in the TF domain. We propose two TF formulations (narrowband and wideband [3]) of the crossrelation. They result in an optimisation problem similar to (1) with a new matrix Bnb or Bwb , built from TF representations of the mixture. Each row of these matrices corresponds to a point in the TF plane. Filter estimation from partial TF information. Assuming that the sources are mutually disjoint in the TF plane, we propose to build for each source a matrix extracted from Bnb (resp. Bwb ) by keeping only the rows indexed by the set Ωj of TF points where the j-th source is the only active one. We then solve the resulting optimisation problem to estimate the filters. IV. E XPERIMENTS The proposed framework combines a TF clustering step, to detect the regions Ωj , with a convex optimisation step, to estimate the sparse filters associated to each source. An experimental evaluation of the proposed approach with real audio data shows that our approach outperforms standard ICA approaches for filter estimation when the filters are sufficiently sparse. R EFERENCES [1] G. Xu, H. Liu, L. Tong, and T. Kailath, “A least-squares approach to blind channel identification,” IEEE Transactions on Signal Processing, vol. 43, no. 12, pp. 2982–2993, 1995. [2] A. Aïssa-El-Bey and K. Abed-Meraim, “Blind simo channel identification using a sparsity criterion,” in Proc. of SPAWC, 2008, pp. 271 – 275. [3] S. Arberet, P. Sudhakar, and R. Gribonval, “A wideband doubly-sparse approach for MITO sparse filter estimation,” in ICASSP’11, 2011.

Estimating multiple filters from stereo mixtures: a double ...

minimise x2 ⋆ a1 − x1 ⋆ a2 2 with a normalisation constraint on the filters [1] (as there is only one source, the source index is dropped on the filters). Denoting. B := B[x1, x2] a matrix built by concatenating Toeplitz matrices derived from the observed mixtures, this leads to the minimisation of B·a2 subject to a2 = 1 where a.

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