3,330 research outputs found
A Fully Time-domain Neural Model for Subband-based Speech Synthesizer
This paper introduces a deep neural network model for subband-based speech
synthesizer. The model benefits from the short bandwidth of the subband signals
to reduce the complexity of the time-domain speech generator. We employed the
multi-level wavelet analysis/synthesis to decompose/reconstruct the signal into
subbands in time domain. Inspired from the WaveNet, a convolutional neural
network (CNN) model predicts subband speech signals fully in time domain. Due
to the short bandwidth of the subbands, a simple network architecture is enough
to train the simple patterns of the subbands accurately. In the ground truth
experiments with teacher-forcing, the subband synthesizer outperforms the
fullband model significantly in terms of both subjective and objective
measures. In addition, by conditioning the model on the phoneme sequence using
a pronunciation dictionary, we have achieved the fully time-domain neural model
for subband-based text-to-speech (TTS) synthesizer, which is nearly end-to-end.
The generated speech of the subband TTS shows comparable quality as the
fullband one with a slighter network architecture for each subband.Comment: 5 pages, 3 figur
Quantifying and containing the curse of high resolution coronal imaging
Future missions such as Solar Orbiter (SO), InterHelioprobe, or Solar Probe
aim at approaching the Sun closer than ever before, with on board some high
resolution imagers (HRI) having a subsecond cadence and a pixel area of about
at the Sun during perihelion. In order to guarantee their scientific
success, it is necessary to evaluate if the photon counts available at these
resolution and cadence will provide a sufficient signal-to-noise ratio (SNR).
We perform a first step in this direction by analyzing and characterizing the
spatial intermittency of Quiet Sun images thanks to a multifractal analysis.
We identify the parameters that specify the scale-invariance behavior. This
identification allows next to select a family of multifractal processes, namely
the Compound Poisson Cascades, that can synthesize artificial images having
some of the scale-invariance properties observed on the recorded images.
The prevalence of self-similarity in Quiet Sun coronal images makes it
relevant to study the ratio between the SNR present at SoHO/EIT images and in
coarsened images. SoHO/EIT images thus play the role of 'high resolution'
images, whereas the 'low-resolution' coarsened images are rebinned so as to
simulate a smaller angular resolution and/or a larger distance to the Sun. For
a fixed difference in angular resolution and in Spacecraft-Sun distance, we
determine the proportion of pixels having a SNR preserved at high resolution
given a particular increase in effective area. If scale-invariance continues to
prevail at smaller scales, the conclusion reached with SoHO/EIT images can be
transposed to the situation where the resolution is increased from SoHO/EIT to
SO/HRI resolution at perihelion.Comment: 25 pages, 1 table, 7 figure
Long-range dependencies in heart rate signals- revisited
The RR series extracted from human electrocardiogram signal (ECG) is
considered as a fractal stochastic process. The manifestation of long-range
dependencies is the presence of power laws in scale dependent process
characteristics. Exponents of these laws: - describing power spectrum
decay, - responsible for decay of detrended fluctuations or
related to, so-called, roughness of a signal, are known to differentiate hearts
of healthy people from hearts with congestive heart failure. There is a strong
expectation that resolution spectrum of exponents, so-called, local exponents
in place of global exponents allows to study differences between hearts in
details. The arguments are given that local exponents obtained in multifractal
analysis by the two methods: wavelet transform modulus maxima (WTMM) and
multifractal detrended fluctuation analysis (MDFA), allow to recognize the
following four stages of the heart: healthy and young, healthy and advance in
years, subjects with left ventricle systolic dysfunction (NYHA I--III class)
and characterized by severe congestive heart failure (NYHA III-IV class).Comment: 24 page
Parametrization of stochastic inputs using generative adversarial networks with application in geology
We investigate artificial neural networks as a parametrization tool for
stochastic inputs in numerical simulations. We address parametrization from the
point of view of emulating the data generating process, instead of explicitly
constructing a parametric form to preserve predefined statistics of the data.
This is done by training a neural network to generate samples from the data
distribution using a recent deep learning technique called generative
adversarial networks. By emulating the data generating process, the relevant
statistics of the data are replicated. The method is assessed in subsurface
flow problems, where effective parametrization of underground properties such
as permeability is important due to the high dimensionality and presence of
high spatial correlations. We experiment with realizations of binary
channelized subsurface permeability and perform uncertainty quantification and
parameter estimation. Results show that the parametrization using generative
adversarial networks is very effective in preserving visual realism as well as
high order statistics of the flow responses, while achieving a dimensionality
reduction of two orders of magnitude
Investigation of delay jitter of heterogeneous traffic in broadband networks
Scope and Methodology of Study: A critical challenge for both wired and wireless networking vendors and carrier companies is to be able to accurately estimate the quality of service (QoS) that will be provided based on the network architecture, router/switch topology, and protocol applied. As a result, this thesis focuses on the theoretical analysis of QoS parameters in term of inter-arrival jitter in differentiated services networks by deploying analytic/mathematical modeling technique and queueing theory, where the analytic model is expressed in terms of a set of equations that can be solved to yield the desired delay jitter parameter. In wireless networks with homogeneous traffic, the effects on the delay jitter in reference to the priority control scheme of the ARQ traffic for the two cases of: 1) the ARQ traffic has a priority over the original transmission traffic; and 2) the ARQ traffic has no priority over the original transmission traffic are evaluated. In wired broadband networks with heterogeneous traffic, the jitter analysis is conducted and the algorithm to control its effect is also developed.Findings and Conclusions: First, the results show that high priority packets always maintain the minimum inter-arrival jitter, which will not be affected even in heavy load situation. Second, the Gaussian traffic modeling is applied using the MVA approach to conduct the queue length analysis, and then the jitter analysis in heterogeneous broadband networks is investigated. While for wireless networks with homogeneous traffic, binomial distribution is used to conduct the queue length analysis, which is sufficient and relatively easy compared to heterogeneous traffic. Third, develop a service discipline called the tagged stream adaptive distortion-reducing peak output-rate enforcing to control and avoid the delay jitter increases without bound in heterogeneous broadband networks. Finally, through the analysis provided, the differential services, was proved not only viable, but also effective to control delay jitter. The analytic models that serve as guidelines to assist network system designers in controlling the QoS requested by customer in term of delay jitter
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