This paper addresses the problem of rendering a virtual source through loudspeaker arrays. The orientation of the virtual source and its aperture determine its radial beampattern. The methodology we present here imposes that the wavefield in a predetermined listening area best approximates the desired wavefield in the least squares sense. With respect to the traditional techniques the number of constraints is much higher than the number of loudspeakers. As a consequence, the loudspeaker coefficient vector is the solution of an over-determined equation system. Moreover this system may be ill-conditioned. In order to solve these issues, we resort to a least squares inversion combined with a Singular Value Decomposition (SVD) to attenuate the problem of ill-conditioning. Some experimental results show the feasibility and the issues of this methodology.

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In this paper a system for continuous analysis, visualization and classification of musical streams is proposed. The system performs visualization and classification task by means of three high-level, semantic features extracted computing a reduction on a multidimensional low-level feature vector through the usage of Gaussian Mixture Models. The visualization of the semantic characteristics of the audio stream has been implemented by mapping the value of the high-level features on a triangular plot and by assigning to each feature a primary color. In this manner, besides having the representation of musical evolution of the signal, we have also obtained representative colors for each musical part of the analyzed streams. The classification exploits a set of one-against-one threedimensional Support Vector Machines trained on some target genres. The obtained results on visualization and classification tasks are very encouraging: our tests on heterogeneous genre streams have shown the validity of proposed approach.

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