23,279 research outputs found

    Robust speech recognition under noisy environments.

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    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

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    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

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    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

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    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 (Xt,Xt−k)(X_t,X_{t-k}) in a process (Xt)t∈Z(X_t)_{t\in\mathbb{Z}}, 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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