This paper presents a method for solving the permutation problem of frequency domain blind source separation (BSS) when the number of source signals is large, and the potential source locations are omnidirectional. We propose a combination of small and large spacing sensor pairs with various axis directions in order to obtain proper geometric information for solving the permutation problem. Exp...

Recognition of planar objects from their images taken from different viewpoints requires affine invariants calculated from the object boundaries. The equivalence between affine transformation and the source mixture model simplifies the recognition problem. The rotation, scaling, skewing, and translation effects of the affine transformation can be undone by using blind source separation (BSS) te...

In this paper, we evaluate the usefulness of several monaural blind source separation (BSS) algorithms in the context of vocal detection (VD). BSS is the problem of recovering several sources, given only a mixture. VD is the problem of automatically identifying the parts in a mixed audio signal, where at least one person is singing. We compare the results of three different strategies for utili...

We consider the problem of joint blind source separation of multiple datasets and introduce an effective solution to the problem. We pose the problem in an independent vector analysis (IVA) framework utilizing the multivariate Gaussian source vector distribution. We provide a new general IVA implementation using a decoupled nonorthogonal optimization algorithm and establish the connection betwe...

This paper deals with estimation of structured signals such as damped sinusoids, exponentials, polynomials, and their products from single channel data. It is shown that building tensors from this kind of data results in tensors with hidden block structure which can be recovered through the tensor diagonalization. The tensor diagonalization means multiplying tensors by several matrices along it...

One of the most important problems in frequency domain blind source separation is the inconsistency across frequency in the permutation of the source estimates. In this paper we present a new algorithm that simultaneously diagonalizes the intrafrequency and the inter-frequency correlation matrices of the source estimates. Experimental results, using speech signals, reveal that the algorithm ach...

This paper deals with the blind source separation problem in convolutive mixtures when the sources are colored processes. In this case, classical methods first extract the innovation processes of the sources and then color them, which yields two successive filter approximations. On the contrary, we propose here a new concept allowing to directly extract estimates of the colored sources in one s...

Source separation by cepstral smoothing of binary masks In this paper, we propose a separation system of speech signals from two convolutive mixtures. The suggested system is based on the combination of blind source separation technique with a time-frequency masking procedure, followed by a smoothing cepstral. Indeed, after separation of signal sources, the estimated binary masks undergo a ceps...