Abstract — The TDOA-based acoustic source localization approach is a powerful and widely-used method which can be applied for one source in several dimensions or several sources in one dimension. However the localization turns out to be more challenging when multiple sound sources should be localized in multiple dimensions, due to a spatial ambiguity phenomenon which requires to perform an intermediate step after the TDOA estimation and before the calculation of the geometrical source positions. In order to obtain the required set of TDOA estimates for the multidimensional localization of multiple sound sources, we apply a recently presented TDOA estimation method based on blind adaptive multiple-input-multiple-output (MIMO) system identification. We demonstrate that this localization method also provides valuable side information which allows us to resolve the spatial ambiguity without any prior knowledge about the source positions. Furthermore we show that the blind adaptive MIMO system identification allows a high spatial resolution. Experimental results for the localization of two sources in a two-dimensional plane show the effectiveness of the proposed scheme. I.

Based on a recently presented generic broadband blind source separation (BSS) algorithm for convolutive mixtures, we propose in this paper a novel algorithm combining advantages of broadband algorithms with the computational efficiency of narrowband techniques. By selective application of the Szegö theorem which relates properties of Toeplitz and circulant matrices, a new normalization is derived as a special case of the generic broadband algorithm. This results in a computationally efficient and fast converging algorithm without introducing typical narrowband problems such as the internal permutation problem or circularity effects. Moreover, a novel regularization method for the generic broadband algorithm is proposed and subsequently also derived for the proposed algorithm. Experimental results in realistic acoustic environments show improved performance of the novel algorithm compared to previous approximations.

We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaural scenario is presented, some already existing and commonly used feedback cancellation performance measures for the unilateral case are adapted to the binaural case, and possible interaction effects between the algorithms are identified. For illustration purposes, a blind source separation algorithm has been chosen as an example for adaptive binaural spatial filtering. Experimental results for binaural hearing aids confirm the theoretical findings and the validity of the new measures.

In the frame of the HearCom 1 project five promising signal enhancement algorithms are validated for future use in hearing instrument devices. To assess the algorithm performance solely based on simulation experiments, a number of physical evaluation measures have been proposed that incorporate basic aspects of normal and impaired human hearing. Additionally, each of the algorithms has been implemented on a common real-time hardware/software platform, which facilitates a profound subjective validation of the algorithm performance. Recently, a multicenter study has been set up across five different test centers in Belgium, the Netherlands, Germany and Switzerland to perceptually evaluate the selected signal enhancement approaches with normally hearing and hearing impaired listeners. 1.

This paper gives an overview on TRINICON, a generic framework for broadband adaptive MIMO filtering, and some of its applications in array processing for speech capture. The motivations for this framework are to bring together the various blind and supervised MIMO adaptation techniques which have been treated largely independently in the literature so far, in order to spotlight their commonalities and relationships, to derive them in a rigorous way from first principles, to facilitate the design of improved systems, and to exploit synergies. In this paper, a special focus is on new results on the so-called Sylvester constraint, an important element of the framework. 1

In this paper the robustness of a previously introduced localization algorithm for multiple acoustic sources is investigated and compared to two popular single-source localization algorithms. To show the versatility of the proposed method, experiments are conducted in various environments with different reverberation times and background noises. In addition, moving speakers are used to show the tracking capability. The application to binaural hearing aids shows the applicability of the algorithm to realistic adverse environments such as, e.g., a cafeteria. 1

Blind source separation (BSS) refers to the problem of recovering signals from several observed linear mixtures (e.g., [1]). In this paper we deal with the convolutive mixing case as encountered, e.g., in acoustic environments,

Abstract—We consider the blind signal separation (BSS) problem of instantaneous mixtures using penalty term and natural gradient. A class of Frobenius norm-based algorithms consisting of the offline/block processing (BP), online processing (OP) algorithms, and their normalized versions is proposed for separating nonstationary and nonwhite signals. The BP and OP algorithms, respectively, suitable for blind separation with offline and online data, are derived by using the nonstationarity and nonwhiteness of signals and the natural gradient method in conjunction with an appropriate penalty term. Associated with almost all algorithms employing a gradient method is a gradient noise problem. We thus develop, from BP and OP, their normalized versions in which the update of an unknown demixing matrix is based on the minimal disturbance principle. We show that the resulting updates are in the same direction as those of the original algorithms but with a scaling factor whose upper bound is unity. Algorithms using the nonstationarity and nonwhiteness properties have been proposed before but, due to the use of logarithms in their derivation, they are not capable of separating signals that are not persistently active and require regularization parameters to mitigate the problem. In this paper, the superior performance of the proposed algorithms to the previously proposed logarithm-based algorithms with and without regularization when separating nonpersistently active source signals is presented through some illustrative numerical experiments. Index Terms—Blind signal separation (BSS), natural gradient methods, penalty term, second-order statistics. I.

A real-time demonstrator for the 2D localization of two sound sources using two microphone pairs is presented and evaluated. The scheme relies on Blind Source Separation (BSS) to adaptively identify the acoustical MIMO system, hence allowing the estimation of relative time delays for each source and each dimension. Extending our previously presented work [1], a mechanism to solve a pairing problem occuring in the multidimensional localization of several sources is described. It exploits the inherent signal extraction abilities of BSS. Experimental evaluations with large microphone apertures show that the demonstrator can accurately localize two speech sources in a 2D space, with a precision better than one degree.