Fast keyword detection with sparse time-frequency models

We address the problem of keyword spotting in continuous speech streams when training and testing conditions can be different. We propose a keyword spotting algorithm based on sparse representation of speech signals in a time-frequency feature space. The training speech elements are jointly represented in a common subspace built on simple basis functions. The subspace is trained in order to capture the common time-frequency structures from different occurrences of the keywords to be spotted. The keyword spotting algorithm then employs a sliding window mechanism on speech streams. It computes the contribution of successive speech segments in the subspace of interest and evaluates the similarity with the training data. Experimental results on the TIMIT database show the effectiveness and the noise resilience of the low complexity spotting algorithm

Sparse aperture masking at the VLT II. Detection limits for the eight debris disks stars β\beta Pic, AU Mic, 49 Cet, η\eta Tel, Fomalhaut, g Lup, HD181327 and HR8799

Context. The formation of planetary systems is a common, yet complex mechanism. Numerous stars have been identified to possess a debris disk, a proto-planetary disk or a planetary system. The understanding of such formation process requires the study of debris disks. These targets are substantial and particularly suitable for optical and infrared observations. Sparse Aperture masking (SAM) is a high angular resolution technique strongly contributing to probe the region from 30 to 200 mas around the stars. This area is usually unreachable with classical imaging, and the technique also remains highly competitive compared to vortex coronagraphy. Aims. We aim to study debris disks with aperture masking to probe the close environment of the stars. Our goal is either to find low mass companions, or to set detection limits. Methods. We observed eight stars presenting debris disks ( $\beta$ Pictoris, AU Microscopii, 49 Ceti, $\eta$ Telescopii, Fomalhaut, g Lupi, HD181327 and HR8799) with SAM technique on the NaCo instrument at the VLT. Results. No close companions were detected using closure phase information under 0.5 of separation from the parent stars. We obtained magnitude detection limits that we converted to Jupiter masses detection limits using theoretical isochrones from evolutionary models. Conclusions. We derived upper mass limits on the presence of companions in the area of few times the diffraction limit of the telescope around each target star.Comment: 7 pages, All magnitude detection limits maps are only available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5

DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity

Nowadays, events usually burst and are propagated online through multiple modern media like social networks and search engines. There exists various research discussing the event dissemination trends on individual medium, while few studies focus on event popularity analysis from a cross-platform perspective. Challenges come from the vast diversity of events and media, limited access to aligned datasets across different media and a great deal of noise in the datasets. In this paper, we design DancingLines, an innovative scheme that captures and quantitatively analyzes event popularity between pairwise text media. It contains two models: TF-SW, a semantic-aware popularity quantification model, based on an integrated weight coefficient leveraging Word2Vec and TextRank; and wDTW-CD, a pairwise event popularity time series alignment model matching different event phases adapted from Dynamic Time Warping. We also propose three metrics to interpret event popularity trends between pairwise social platforms. Experimental results on eighteen real-world event datasets from an influential social network and a popular search engine validate the effectiveness and applicability of our scheme. DancingLines is demonstrated to possess broad application potentials for discovering the knowledge of various aspects related to events and different media

UVMULTIFIT: A versatile tool for fitting astronomical radio interferometric data

The analysis of astronomical interferometric data is often performed on the images obtained after deconvolution of the interferometer's point spread function (PSF). This strategy can be understood (especially for cases of sparse arrays) as fitting models to models, since the deconvolved images are already non-unique model representations of the actual data (i.e., the visibilities). Indeed, the interferometric images may be affected by visibility gridding, weighting schemes (e.g., natural vs. uniform), and the particulars of the (non-linear) deconvolution algorithms. Fitting models to the direct interferometric observables (i.e., the visibilities) is preferable in the cases of simple (analytical) sky intensity distributions. In this paper, we present UVMULTIFIT, a versatile library for fitting visibility data, implemented in a Python-based framework. Our software is currently based on the CASA package, but can be easily adapted to other analysis packages, provided they have a Python API. We have tested the software with synthetic data, as well as with real observations. In some cases (e.g., sources with sizes smaller than the diffraction limit of the interferometer), the results from the fit to the visibilities (e.g., spectra of close by sources) are far superior to the output obtained from the mere analysis of the deconvolved images. UVMULTIFIT is a powerful improvement of existing tasks to extract the maximum amount of information from visibility data, especially in cases close to the sensitivity/resolution limits of interferometric observations.Comment: 10 pages, 4 figures. Accepted in A&A. Code available at http://nordic-alma.se/support/software-tool