23,279 research outputs found
Robust speech recognition under noisy environments.
Lee Siu Wa.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 116-121).Abstracts in English and Chinese.Abstract --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- An Overview on Automatic Speech Recognition --- p.2Chapter 1.2 --- Thesis Outline --- p.6Chapter 2 --- Baseline Speech Recognition System --- p.8Chapter 2.1 --- Baseline Speech Recognition Framework --- p.8Chapter 2.2 --- Acoustic Feature Extraction --- p.11Chapter 2.2.1 --- Speech Production and Source-Filter Model --- p.12Chapter 2.2.2 --- Review of Feature Representations --- p.14Chapter 2.2.3 --- Mel-frequency Cepstral Coefficients --- p.20Chapter 2.2.4 --- Energy and Dynamic Features --- p.24Chapter 2.3 --- Back-end Decoder --- p.26Chapter 2.4 --- English Digit String Corpus ´ؤ AURORA2 --- p.28Chapter 2.5 --- Baseline Recognition Experiment --- p.31Chapter 3 --- A Simple Recognition Framework with Model Selection --- p.34Chapter 3.1 --- Mismatch between Training and Testing Conditions --- p.34Chapter 3.2 --- Matched Training and Testing Conditions --- p.38Chapter 3.2.1 --- Noise type-Matching --- p.38Chapter 3.2.2 --- SNR-Matching --- p.43Chapter 3.2.3 --- Noise Type and SNR-Matching --- p.44Chapter 3.3 --- Recognition Framework with Model Selection --- p.48Chapter 4 --- Noise Spectral Estimation --- p.53Chapter 4.1 --- Introduction to Statistical Estimation Methods --- p.53Chapter 4.1.1 --- Conventional Estimation Methods --- p.54Chapter 4.1.2 --- Histogram Technique --- p.55Chapter 4.2 --- Quantile-based Noise Estimation (QBNE) --- p.57Chapter 4.2.1 --- Overview of Quantile-based Noise Estimation (QBNE) --- p.58Chapter 4.2.2 --- Time-Frequency Quantile-based Noise Estimation (T-F QBNE) --- p.62Chapter 4.2.3 --- Mainlobe-Resilient Time-Frequency Quantile-based Noise Estimation (M-R T-F QBNE) --- p.65Chapter 4.3 --- Estimation Performance Analysis --- p.72Chapter 4.4 --- Recognition Experiment with Model Selection --- p.74Chapter 5 --- Feature Compensation: Algorithm and Experiment --- p.81Chapter 5.1 --- Feature Deviation from Clean Speech --- p.81Chapter 5.1.1 --- Deviation in MFCC Features --- p.82Chapter 5.1.2 --- Implications for Feature Compensation --- p.84Chapter 5.2 --- Overview of Conventional Compensation Methods --- p.86Chapter 5.3 --- Feature Compensation by In-phase Feature Induction --- p.94Chapter 5.3.1 --- Motivation --- p.94Chapter 5.3.2 --- Methodology --- p.97Chapter 5.4 --- Compensation Framework for Magnitude Spectrum and Segmen- tal Energy --- p.102Chapter 5.5 --- Recognition -Experiments --- p.103Chapter 6 --- Conclusions --- p.112Chapter 6.1 --- Summary and Discussions --- p.112Chapter 6.2 --- Future Directions --- p.114Bibliography --- p.11
Advancing image quantification methods and tools for analysis of nanoparticle electrokinetics
Image processing methods and techniques for high-throughput quantification of dielectrophoretic (DEP) collections onto planar castellated electrode arrays are developed and evaluated. Fluorescence-based dielectrophoretic spectroscopy is an important tool for laboratory investigations of AC electrokinetic properties of nanoparticles. This paper details new, first principle, theoretical and experimental developments of geometric feature recognition techniques that enable quantification of positive dielectrophoretic (pDEP) nanoparticle collections onto castellated arrays. As an alternative to the geometric-based method, novel statistical methods that do not require any information about array features, are also developed using the quantile and standard deviation functions. Data from pDEP collection and release experiments using 200 nm diameter latex nanospheres demonstrates that pDEP quantification using the statistic-based methods yields quantitatively similar results to the geometric-based method. The development of geometric- and statistic-based quantification methods enables high-throughput, supervisor-free image processing tools critical for dielectrophoretic spectroscopy and automated DEP technology development
Parameter estimation for coalescing massive binary black holes with LISA using the full 2-post-Newtonian gravitational waveform and spin-orbit precession
With one exception, previous analyses of the measurement accuracy of
gravitational wave experiments for comparable-mass binary systems have
neglected either spin-precession effects or subdominant harmonics and amplitude
modulations. Here we give the first explicit description of how these effects
combine to improve parameter estimation. We consider supermassive black hole
binaries as expected to be observed with the planned space-based interferometer
LISA, and study the measurement accuracy for several astrophysically
interesting parameters obtainable taking into account the full 2PN waveform for
spinning bodies, as well as spin-precession effects. We find that for binaries
with a total mass in the range 10^5 M_Sun < M < 10^7 M_Sun at a redshift of 1,
a factor ~1.5 is in general gained in accuracy, with the notable exception of
the determination of the individual masses in equal-mass systems, for which a
factor ~5 can be gained. We also find, as could be expected, that using the
full waveform helps increasing the upper mass limit for detection, which can be
as high as M = 10^8 M_Sun at a redshift of 1, as well as the redshift limit
where some information can be extracted from a system, which is roughly z = 10
for M < 10^7 M_Sun, 1.5-5 times higher than with the restricted waveform. We
computed that the full waveform allows to use supermassive black hole binaries
as standard sirens up to a redshift of z = 1.6, about 0.4 larger than what
previous studies allowed. We found that for lower unequal-mass binary systems,
the measurement accuracy is not as drastically improved as for other systems.
This suggests that for these systems, adding parameters such as eccentricity or
alternative gravity parameters could be achieved without much loss in the
accuracy.Comment: 20 pages, 20 figure
Quantile spectral processes: Asymptotic analysis and inference
Quantile- and copula-related spectral concepts recently have been considered
by various authors. Those spectra, in their most general form, provide a full
characterization of the copulas associated with the pairs in a
process , and account for important dynamic features,
such as changes in the conditional shape (skewness, kurtosis),
time-irreversibility, or dependence in the extremes that their traditional
counterparts cannot capture. Despite various proposals for estimation
strategies, only quite incomplete asymptotic distributional results are
available so far for the proposed estimators, which constitutes an important
obstacle for their practical application. In this paper, we provide a detailed
asymptotic analysis of a class of smoothed rank-based cross-periodograms
associated with the copula spectral density kernels introduced in Dette et al.
[Bernoulli 21 (2015) 781-831]. We show that, for a very general class of
(possibly nonlinear) processes, properly scaled and centered smoothed versions
of those cross-periodograms, indexed by couples of quantile levels, converge
weakly, as stochastic processes, to Gaussian processes. A first application of
those results is the construction of asymptotic confidence intervals for copula
spectral density kernels. The same convergence results also provide asymptotic
distributions (under serially dependent observations) for a new class of
rank-based spectral methods involving the Fourier transforms of rank-based
serial statistics such as the Spearman, Blomqvist or Gini autocovariance
coefficients.Comment: Published at http://dx.doi.org/10.3150/15-BEJ711 in the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
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