22,770 research outputs found
EMD-based filtering (EMDF) of low-frequency noise for speech enhancement
An Empirical Mode Decomposition based filtering (EMDF) approach is presented as a post-processing stage for speech enhancement. This method is particularly effective in low frequency noise environments. Unlike previous EMD based denoising methods, this approach does not make the assumption that the contaminating noise signal is fractional Gaussian Noise. An adaptive method is developed to select the IMF index for separating the noise components from the speech based on the second-order IMF statistics. The low frequency noise components are then separated by a partial reconstruction from the IMFs. It is shown that the proposed EMDF technique is able to suppress residual noise from speech signals that were enhanced by the conventional optimallymodified log-spectral amplitude approach which uses a minimum statistics based noise estimate. A comparative performance study is included that demonstrates the effectiveness of the EMDF system in various noise environments, such as car interior noise, military vehicle noise and babble noise. In particular, improvements up to 10 dB are obtained in car noise environments. Listening tests were performed that confirm the results
Cosmological constraints from the capture of non-Gaussianity in Weak Lensing data
Weak gravitational lensing has become a common tool to constrain the
cosmological model. The majority of the methods to derive constraints on
cosmological parameters use second-order statistics of the cosmic shear.
Despite their success, second-order statistics are not optimal and degeneracies
between some parameters remain. Tighter constraints can be obtained if
second-order statistics are combined with a statistic that is efficient to
capture non-Gaussianity. In this paper, we search for such a statistical tool
and we show that there is additional information to be extracted from
statistical analysis of the convergence maps beyond what can be obtained from
statistical analysis of the shear field. For this purpose, we have carried out
a large number of cosmological simulations along the {\sigma}8-{\Omega}m
degeneracy, and we have considered three different statistics commonly used for
non-Gaussian features characterization: skewness, kurtosis and peak count. To
be able to investigate non-Gaussianity directly in the shear field we have used
the aperture mass definition of these three statistics for different scales.
Then, the results have been compared with the results obtained with the same
statistics estimated in the convergence maps at the same scales. First, we show
that shear statistics give similar constraints to those given by convergence
statistics, if the same scale is considered. In addition, we find that the peak
count statistic is the best to capture non-Gaussianities in the weak lensing
field and to break the {\sigma}8-{\Omega}m degeneracy. We show that this
statistical analysis should be conducted in the convergence maps: first,
because there exist fast algorithms to compute the convergence map for
different scales, and secondly because it offers the opportunity to denoise the
reconstructed convergence map, which improves non-Gaussian features extraction.Comment: Accepted for publication in MNRAS (11 pages, 5 figures, 9 tables
Learning sparse representations of depth
This paper introduces a new method for learning and inferring sparse
representations of depth (disparity) maps. The proposed algorithm relaxes the
usual assumption of the stationary noise model in sparse coding. This enables
learning from data corrupted with spatially varying noise or uncertainty,
typically obtained by laser range scanners or structured light depth cameras.
Sparse representations are learned from the Middlebury database disparity maps
and then exploited in a two-layer graphical model for inferring depth from
stereo, by including a sparsity prior on the learned features. Since they
capture higher-order dependencies in the depth structure, these priors can
complement smoothness priors commonly used in depth inference based on Markov
Random Field (MRF) models. Inference on the proposed graph is achieved using an
alternating iterative optimization technique, where the first layer is solved
using an existing MRF-based stereo matching algorithm, then held fixed as the
second layer is solved using the proposed non-stationary sparse coding
algorithm. This leads to a general method for improving solutions of state of
the art MRF-based depth estimation algorithms. Our experimental results first
show that depth inference using learned representations leads to state of the
art denoising of depth maps obtained from laser range scanners and a time of
flight camera. Furthermore, we show that adding sparse priors improves the
results of two depth estimation methods: the classical graph cut algorithm by
Boykov et al. and the more recent algorithm of Woodford et al.Comment: 12 page
DUDE-Seq: Fast, Flexible, and Robust Denoising for Targeted Amplicon Sequencing
We consider the correction of errors from nucleotide sequences produced by
next-generation targeted amplicon sequencing. The next-generation sequencing
(NGS) platforms can provide a great deal of sequencing data thanks to their
high throughput, but the associated error rates often tend to be high.
Denoising in high-throughput sequencing has thus become a crucial process for
boosting the reliability of downstream analyses. Our methodology, named
DUDE-Seq, is derived from a general setting of reconstructing finite-valued
source data corrupted by a discrete memoryless channel and effectively corrects
substitution and homopolymer indel errors, the two major types of sequencing
errors in most high-throughput targeted amplicon sequencing platforms. Our
experimental studies with real and simulated datasets suggest that the proposed
DUDE-Seq not only outperforms existing alternatives in terms of
error-correction capability and time efficiency, but also boosts the
reliability of downstream analyses. Further, the flexibility of DUDE-Seq
enables its robust application to different sequencing platforms and analysis
pipelines by simple updates of the noise model. DUDE-Seq is available at
http://data.snu.ac.kr/pub/dude-seq
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