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The improvement of digital communication networks increased the interest towards Networked Music Performance both for entertainment and education. One of the main issues that affects the effectiveness of remote performances is the inherent network latency in the communication chain. In this paper we propose a technique that, to the best of our knowledge, is the first work that explores the use of adaptive metronomes in order to track the tempo of the musicians and help them in coping with the latency. We test our proposal with preliminary experiments in the context of chamber music.

In this paper we propose an algorithm for the reconstruction of the Ray Space Transform (RST) through the use of neural networks. In particular, our aim is to reconstruct the magnitude of the RST acquired from a linear microphone array, as if the array were composed by a larger amount of microphones. This is useful for applications that need a higher RST resolution when only a limited amount of microphones can be used due to practical constraints or physical limitations. The proposed solution leverages recent advancements in deep learning as it is based on a fully convolutional autoencoder. To validate our method, we show through a simulative campaign that it is possible to improve sound source localization using the reconstructed RST compared to the use of the original RST.

With the big improvement of digital communication networks, Networked Music Performances (NMP) received a great interest from music live performance and music recording industry. The positive impact of NMP in pedagogical appli- cations, instead, has been only preliminary explored. Within the InterMUSIC project, we aim to investigate NMP from a pedagogical perspective, that has considerable differences with respect to music performances, and to develop tools to improve distance learning experiences. In this paper, we introduce a conceptual framework designed to be the foundation for all the experiments conducted in the project. We also present two preliminary experiments that investigate the sense of presence of geographically-distant musicians in a distance learning scenario. We discuss the comments provided by the musicians as a set of requirements and guidelines for future experiments.

We present an approach for merging into a single super-image a set of uncalibrated images of a general 3D scene taken from multiple viewpoints. To this aim, the content of either image is augmented with visual information taken from the others, while maintaining projective coherence. The approach extends the usual mosaicing techniques to image collections with3D parallax, and operates like a virtual sensor provided with an enlarged field of view and the capability of seeing through visual occlusions in an "X-ray" fashion. Fundamental matrices are used to transfer visual information through the vertexes of an image graph. A dense stereo paradigm is employed to achieve photorealism by partitioning image pairs into corresponding regions. Results in oriented projective geometry are then exploited to both detect and handle occlusions by assessing the visibility properties of each transferred point.

We describe a low cost system for metric 3D scanning from uncalibrated images based on rotational kinematic constraints. The system is composed by a turntable, an offthe- shelf camera and a laser stripe illuminator. System operation is based on the construction of the virtual image of a surface of revolution (SOR), from which two imaged SOR cross-sections are obtained in an automatic way, and internal camera calibration is performed by exploiting the same object being scanned. Shape acquisition is finally obtained by laser profile rectification and collation. Experiments with real data are shown, providing an insight into both camera calibration and shape reconstruction performance. System accuracy appears to be adequate for desktop applications.

In this paper, we address the problem of the automatic metric reconstruction Surface of Revolution (SOR) from a single uncalibrated view. The apparent contour and the visible portions of the imaged SOR cross sections are extracted and classified. The harmonic homology that models the image projection of the SOR is also estimated. The special care devoted to accuracy and robustness with respect to outliers makes the approach suitable for automatic camera calibration and metric reconstruction from single uncalibrated views of a SOR. Robustness and accuracy are obtained by embedding a graph-based grouping strategy (Euclidean Minimum Spanning Tree) into an Iterative Closest Point framework for projective curve alignment at multiple scales. Classification of SOR curves is achieved through a 2-dof voting scheme based on a pencil of conics novel parametrization. The main contribution of this work is to extend the domain of automatic single view reconstruction from piecewise planar scenes to scenes including curved surfaces, thus allowing to create automatically realistic image models of man-made objects. Experimental results with real images taken from the internet are reported, and the effectiveness and limitations of the approach are discussed.