38,483 research outputs found
Kalman tracking of linear predictor and harmonic noise models for noisy speech enhancement
This paper presents a speech enhancement method based on the tracking and denoising of the formants of a linear prediction (LP) model of the spectral envelope of speech and the parameters of a harmonic noise model (HNM) of its excitation. The main advantages of tracking and denoising the prominent energy contours of speech are the efficient use of the spectral and temporal structures of successive speech frames and a mitigation of processing artefact known as the ‘musical noise’ or ‘musical tones’.The formant-tracking linear prediction (FTLP) model estimation consists of three stages: (a) speech pre-cleaning based on a spectral amplitude estimation, (b) formant-tracking across successive speech frames using the Viterbi method, and (c) Kalman filtering of the formant trajectories across successive speech frames.The HNM parameters for the excitation signal comprise; voiced/unvoiced decision, the fundamental frequency, the harmonics’ amplitudes and the variance of the noise component of excitation. A frequency-domain pitch extraction method is proposed that searches for the peak signal to noise ratios (SNRs) at the harmonics. For each speech frame several pitch candidates are calculated. An estimate of the pitch trajectory across successive frames is obtained using a Viterbi decoder. The trajectories of the noisy excitation harmonics across successive speech frames are modeled and denoised using Kalman filters.The proposed method is used to deconstruct noisy speech, de-noise its model parameters and then reconstitute speech from its cleaned parts. Experimental evaluations show the performance gains of the formant tracking, pitch extraction and noise reduction stages
Regression analysis with missing data and unknown colored noise: application to the MICROSCOPE space mission
The analysis of physical measurements often copes with highly correlated
noises and interruptions caused by outliers, saturation events or transmission
losses. We assess the impact of missing data on the performance of linear
regression analysis involving the fit of modeled or measured time series. We
show that data gaps can significantly alter the precision of the regression
parameter estimation in the presence of colored noise, due to the frequency
leakage of the noise power. We present a regression method which cancels this
effect and estimates the parameters of interest with a precision comparable to
the complete data case, even if the noise power spectral density (PSD) is not
known a priori. The method is based on an autoregressive (AR) fit of the noise,
which allows us to build an approximate generalized least squares estimator
approaching the minimal variance bound. The method, which can be applied to any
similar data processing, is tested on simulated measurements of the MICROSCOPE
space mission, whose goal is to test the Weak Equivalence Principle (WEP) with
a precision of . In this particular context the signal of interest is
the WEP violation signal expected to be found around a well defined frequency.
We test our method with different gap patterns and noise of known PSD and find
that the results agree with the mission requirements, decreasing the
uncertainty by a factor 60 with respect to ordinary least squares methods. We
show that it also provides a test of significance to assess the uncertainty of
the measurement.Comment: 12 pages, 4 figures, to be published in Phys. Rev.
Impact of environmental inputs on reverse-engineering approach to network structures
Background: Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs.
Results: With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism.
Conclusion: We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations
Multibaseline gravitational wave radiometry
We present a statistic for the detection of stochastic gravitational wave
backgrounds (SGWBs) using radiometry with a network of multiple baselines. We
also quantitatively compare the sensitivities of existing baselines and their
network to SGWBs. We assess how the measurement accuracy of signal parameters,
e.g., the sky position of a localized source, can improve when using a network
of baselines, as compared to any of the single participating baselines. The
search statistic itself is derived from the likelihood ratio of the cross
correlation of the data across all possible baselines in a detector network and
is optimal in Gaussian noise. Specifically, it is the likelihood ratio
maximized over the strength of the SGWB, and is called the maximized-likelihood
ratio (MLR). One of the main advantages of using the MLR over past search
strategies for inferring the presence or absence of a signal is that the former
does not require the deconvolution of the cross correlation statistic.
Therefore, it does not suffer from errors inherent to the deconvolution
procedure and is especially useful for detecting weak sources. In the limit of
a single baseline, it reduces to the detection statistic studied by Ballmer
[Class. Quant. Grav. 23, S179 (2006)] and Mitra et al. [Phys. Rev. D 77, 042002
(2008)]. Unlike past studies, here the MLR statistic enables us to compare
quantitatively the performances of a variety of baselines searching for a SGWB
signal in (simulated) data. Although we use simulated noise and SGWB signals
for making these comparisons, our method can be straightforwardly applied on
real data.Comment: 17 pages and 19 figure
Spherical Slepian functions and the polar gap in geodesy
The estimation of potential fields such as the gravitational or magnetic
potential at the surface of a spherical planet from noisy observations taken at
an altitude over an incomplete portion of the globe is a classic example of an
ill-posed inverse problem. Here we show that the geodetic estimation problem
has deep-seated connections to Slepian's spatiospectral localization problem on
the sphere, which amounts to finding bandlimited spherical functions whose
energy is optimally concentrated in some closed portion of the unit sphere.
This allows us to formulate an alternative solution to the traditional damped
least-squares spherical harmonic approach in geodesy, whereby the source field
is now expanded in a truncated Slepian function basis set. We discuss the
relative performance of both methods with regard to standard statistical
measures as bias, variance and mean-square error, and pay special attention to
the algorithmic efficiency of computing the Slepian functions on the region
complementary to the axisymmetric polar gap characteristic of satellite
surveys. The ease, speed, and accuracy of this new method makes the use of
spherical Slepian functions in earth and planetary geodesy practical.Comment: 14 figures, submitted to the Geophysical Journal Internationa
A Bayesian parameter estimation approach to pulsar time-of-arrival analysis
The increasing sensitivities of pulsar timing arrays to ultra-low frequency
(nHz) gravitational waves promises to achieve direct gravitational wave
detection within the next 5-10 years. While there are many parallel efforts
being made in the improvement of telescope sensitivity, the detection of stable
millisecond pulsars and the improvement of the timing software, there are
reasons to believe that the methods used to accurately determine the
time-of-arrival (TOA) of pulses from radio pulsars can be improved upon. More
specifically, the determination of the uncertainties on these TOAs, which
strongly affect the ability to detect GWs through pulsar timing, may be
unreliable. We propose two Bayesian methods for the generation of pulsar TOAs
starting from pulsar "search-mode" data and pre-folded data. These methods are
applied to simulated toy-model examples and in this initial work we focus on
the issue of uncertainties in the folding period. The final results of our
analysis are expressed in the form of posterior probability distributions on
the signal parameters (including the TOA) from a single observation.Comment: 16 pages, 4 figure
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