A Geometrically Constrained ICA Algorithm for Blind Separation in Convolutive Environments Michele SCARPINITI 1 , Francesco DI PALMA, Raffaele PARISI and Aurelio UNCINI Infocom Department, “Sapienza” University of Rome Abstract. In this paper a blind source separation algorithm in convolutive environ- ment is presented. In order to avoid the classical permutation ambiguity in the fre- quency domain solution, a geometrical constraint is considered. Moreover a beam- former algorithm is integrated with the proposed solution: in this way the directiv- ity pattern of the proposed architecture can take into account the residual permuta- tion at low frequencies and the scaling inconsistency. Several experimental results are shown to demonstrate the effectiveness of the proposed method. Keywords. Blind Source Separation, Convolutive environment, Frequency domain algorithms, Geometrical constraints, FastICA. Introduction An increasingly interest on Blind Source Separation (BSS) has arisen from signal pro- cessing researchers in the last fifteen years and a huge number of works were published [4,6]. A particular and emerging field of applications for signal processing is hands-free communication particularly useful in a lot of practical situations, such as a teleconfer- ence or a vehicle environment. In these applications a speech enhancement is necessary, because usually several sources are captured. In this way BSS is a promising technique for speech enhancement in adverse environments, like highly reverberant rooms. While there are a lot of studies in the linear and instantaneous case, much poor is the range of works in convolutive environment. To achieve BSS of convolutive mixtures, several methods have been proposed [2,6]. In order to solve the BSS problem in a reason- able amount of time the problem is transformed into the frequency domain: the algorithm solves an instantaneous BSS problem for every frequency simultaneously [11,21]. Unfortunately in frequency domain two trivial ambiguities occur that could be par- ticular troublesome [9,10]. The permutation ambiguity is particularly tiresome: when converting signal to time domain, contributions from different sources will appear into a single channel, thus destroying the separation achieved in the frequency domain; in addition the scaling indeterminacy at each frequency bin will result in an overall filtering of the sources. Different solutions to these problems can be found in literature [12,16]. 1 Corresponding Author: Infocom Department, “Sapienza” University of Rome, via Eudossiana 18, 00184 Roma; Tel. +39 0644585869; e-mail: michele.scarpiniti@uniroma1.it.