A Generative Product-of-Filters Model of Audio

We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain. PoF makes similar assumptions to those used in the classic homomorphic filtering approach to signal processing, but replaces hand-designed decompositions built of basic signal processing operations with a learned decomposition based on statistical inference. This paper formulates the PoF model and derives a mean-field method for posterior inference and a variational EM algorithm to estimate the model's free parameters. We demonstrate PoF's potential for audio processing on a bandwidth expansion task, and show that PoF can serve as an effective unsupervised feature extractor for a speaker identification task.Comment: ICLR 2014 conference-track submission. Added link to the source cod

Universal Denoising Networks : A Novel CNN Architecture for Image Denoising

We design a novel network architecture for learning discriminative image models that are employed to efficiently tackle the problem of grayscale and color image denoising. Based on the proposed architecture, we introduce two different variants. The first network involves convolutional layers as a core component, while the second one relies instead on non-local filtering layers and thus it is able to exploit the inherent non-local self-similarity property of natural images. As opposed to most of the existing deep network approaches, which require the training of a specific model for each considered noise level, the proposed models are able to handle a wide range of noise levels using a single set of learned parameters, while they are very robust when the noise degrading the latent image does not match the statistics of the noise used during training. The latter argument is supported by results that we report on publicly available images corrupted by unknown noise and which we compare against solutions obtained by competing methods. At the same time the introduced networks achieve excellent results under additive white Gaussian noise (AWGN), which are comparable to those of the current state-of-the-art network, while they depend on a more shallow architecture with the number of trained parameters being one order of magnitude smaller. These properties make the proposed networks ideal candidates to serve as sub-solvers on restoration methods that deal with general inverse imaging problems such as deblurring, demosaicking, superresolution, etc.Comment: Camera ready paper to appear in the Proceedings of CVPR 201

Forecasting Hungarian Export Volume

The paper summarizes the research on forecasting the Hungarian export volume. We elaborated a two-step procedure. In the first step we forecasted foreign demand, then in the second step we forecasted Hungarian export using the best outcome of the first step together with real exchange rate and import series. We used several econometric techniques and tested our results statistically by two criteria. We compared the precision and stability of the different forecasts. The ARIMA forecasts were employed as a benchmark. We found that in terms of both criteria foreign demand forecasts were significantly better than those obtained with ARIMA. However, in the case of the Hungarian export volume our results were only better in terms of the stability properties. Therefore the choice between the different forecasting methods was not obvious, so a ’Consensus’ index was also computed as a weighted average of different forecasts, where the weights were negative functions of imprecision and instability.

A UV study of nearby luminous infrared galaxies: star formation histories and the role of AGN

We employ UV and optical photometry, from the GALEX and SDSS surveys respectively, to study the star formation histories of 561 luminous infrared galaxies (LIRGs) in the nearby Universe. A small fraction (~4%) of these galaxies have spheroidal or near-spheroidal morphologies and could be progenitors of elliptical galaxies. The remaining galaxies are morphologically late-type or ongoing mergers. 61% of the LIRGs do not show signs of interactions, while the remaining objects are either interacting (~18%) or show post-merger morphologies (~19%). The (SSP-weighted) average age of the underlying stellar populations in these objects is typically 5-9 Gyrs, with a mean value of ~6.8 Gyrs. ~60% of the LIRG population began their recent star formation (RSF) episode within the last Gyr, while the remaining objects began their RSF episodes 1 to 3 Gyrs in the past. Up to 35% of the stellar mass in the remnant forms in these episodes - the mean value is ~15%. The (decay) timescales of the star formation are typically a few Gyrs, indicating that the star formation rate does not decline significantly during the course of the burst. 14% of the LIRG population host (Type 2) AGN. The AGN hosts exhibit UV and optical colours that are redder than those of the normal (non-AGN) population. However, there is no evidence for a systematically higher dust content in the AGN hosts. AGN typically appear ~0.5-0.7 Gyrs after the onset of star formation and the redder colours are a result of older RSF episodes, with no measurable evidence of negative feedback from the AGN on the star formation in their host galaxies. (abridged)Comment: MNRAS in press. Some figures degraded, high resolution version available at: http://www-astro.physics.ox.ac.uk/~skaviraj/PAPERS/lirgs_sdss.pd

Deep Boosting: Layered Feature Mining for General Image Classification

Constructing effective representations is a critical but challenging problem in multimedia understanding. The traditional handcraft features often rely on domain knowledge, limiting the performances of exiting methods. This paper discusses a novel computational architecture for general image feature mining, which assembles the primitive filters (i.e. Gabor wavelets) into compositional features in a layer-wise manner. In each layer, we produce a number of base classifiers (i.e. regression stumps) associated with the generated features, and discover informative compositions by using the boosting algorithm. The output compositional features of each layer are treated as the base components to build up the next layer. Our framework is able to generate expressive image representations while inducing very discriminate functions for image classification. The experiments are conducted on several public datasets, and we demonstrate superior performances over state-of-the-art approaches.Comment: 6 pages, 4 figures, ICME 201