Music-Related Media-Contents Synchronization over theWeb: the IEEE 1599 Initiative

IEEE 1599 is an international standard originally conceived for music, which aims at providing a comprehensive description of the media contents related to a music piece within a multi-layer and synchronized environment. A number of o_- line and stand-alone software prototypes has been realized after its standardization, occurred in 2008. Recently, thanks to some technological advances (e.g. the release of HTML5), the engine of the IEEE 1599 parser has been ported on the Web. Some non-trivial problems have been solved, e.g. the management of multiple simultaneous media streams in a client-server architecture. After providing an overview of the IEEE 1599 standard, this article presents a survey of the recent initiatives regarding audio-driven synchronization over the Web

Computing the Stereo Matching Cost with a Convolutional Neural Network

We present a method for extracting depth information from a rectified image pair. We train a convolutional neural network to predict how well two image patches match and use it to compute the stereo matching cost. The cost is refined by cross-based cost aggregation and semiglobal matching, followed by a left-right consistency check to eliminate errors in the occluded regions. Our stereo method achieves an error rate of 2.61 % on the KITTI stereo dataset and is currently (August 2014) the top performing method on this dataset.Comment: Conference on Computer Vision and Pattern Recognition (CVPR), June 201

Extending Gossip Algorithms to Distributed Estimation of U-Statistics

Efficient and robust algorithms for decentralized estimation in networks are essential to many distributed systems. Whereas distributed estimation of sample mean statistics has been the subject of a good deal of attention, computation of $U$-statistics, relying on more expensive averaging over pairs of observations, is a less investigated area. Yet, such data functionals are essential to describe global properties of a statistical population, with important examples including Area Under the Curve, empirical variance, Gini mean difference and within-cluster point scatter. This paper proposes new synchronous and asynchronous randomized gossip algorithms which simultaneously propagate data across the network and maintain local estimates of the $U$-statistic of interest. We establish convergence rate bounds of $O(1/t)$ and $O(\log t / t)$ for the synchronous and asynchronous cases respectively, where $t$ is the number of iterations, with explicit data and network dependent terms. Beyond favorable comparisons in terms of rate analysis, numerical experiments provide empirical evidence the proposed algorithms surpasses the previously introduced approach.Comment: to be presented at NIPS 201

On the Adoption of Standard Encoding Formats to Ensure Interoperability of Music Digital Archives: The IEEE 1599 Format

With this paper, we want to stimulate the discussion about technologies for inter-operation between various music datasets and collections. Among the many standards for music representation, IEEE 1599 is the only one which was born with the exact purpose of representing the heterogeneous structures of music documents, granting full synchronization of all the different aspects of music (audio recordings, sheet music images, symbolic representations, musicological analysis, etc). We propose the adoption of IEEE 1599 as an interoperability framework between different collections for advanced music experience, musicological applications, and Music Information Retrieval (MIR). In the years to come, the format will undergo a review process aimed at providing an updated/improved version. It is now the perfect time, for all the stakeholders, to come together and discuss how the format can evolve to better support their requirements, enhancing its descriptive strength and available tools. Moreover, this standard can be profitably applied to any field that requires multi-layer and synchronized descriptions

Doubly Stochastic Variational Inference for Deep Gaussian Processes

Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference algorithm, which does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression.Comment: NIPS 201