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Review of Unbiased FIR Filters, Smoothers, and Predictors for Polynomial Signals
Extracting an estimate of a slowly varying signal corrupted by noise is a common task. Examples can be found in industrial, scientific and biomedical instrumentation. Depending on the nature of the application the signal estimate is allowed to be a delayed estimate of the original signal or, in the other extreme, no delay is tolerated. These cases are commonly referred to as filtering, prediction, and smoothing depending on the amount of advance or lag between the input data set and the output data set. In this review paper we provide a comprehensive set of design and analysis tools for designing unbiased FIR filters, predictors, and smoothers for slowly varying signals, i.e. signals that can be modeled by low order polynomials. Explicit expressions of parameters needed in practical implementations are given. Real life examples are provided including cases where the method is extended to signals that are piecewise slowly varying. A critical view on recursive implementations of the algorithms is provided
Speaker recognition using frequency filtered spectral energies
The spectral parameters that result from filtering the
frequency sequence of log mel-scaled filter-bank energies
with a simple first or second order FIR filter have proved
to be an efficient speech representation in terms of both
speech recognition rate and computational load. Recently,
the authors have shown that this frequency filtering can
approximately equalize the cepstrum variance enhancing
the oscillations of the spectral envelope curve that are
most effective for discrimination between speakers. Even
better speaker identification results than using melcepstrum
have been obtained on the TIMIT database,
especially when white noise was added. On the other
hand, the hybridization of both linear prediction and
filter-bank spectral analysis using either cepstral
transformation or the alternative frequency filtering has
been explored for speaker verification. The combination
of hybrid spectral analysis and frequency filtering, that
had shown to be able to outperform the conventional
techniques in clean and noisy word recognition, has yield
good text-dependent speaker verification results on the
new speaker-oriented telephone-line POLYCOST
database.Peer ReviewedPostprint (published version
A Subband-Based SVM Front-End for Robust ASR
This work proposes a novel support vector machine (SVM) based robust
automatic speech recognition (ASR) front-end that operates on an ensemble of
the subband components of high-dimensional acoustic waveforms. The key issues
of selecting the appropriate SVM kernels for classification in frequency
subbands and the combination of individual subband classifiers using ensemble
methods are addressed. The proposed front-end is compared with state-of-the-art
ASR front-ends in terms of robustness to additive noise and linear filtering.
Experiments performed on the TIMIT phoneme classification task demonstrate the
benefits of the proposed subband based SVM front-end: it outperforms the
standard cepstral front-end in the presence of noise and linear filtering for
signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed
front-end with a conventional front-end such as MFCC yields further
improvements over the individual front ends across the full range of noise
levels
A Kalman Filter Approach for Biomolecular Systems with Noise Covariance Updating
An important part of system modeling is determining parameter values,
particularly for biomolecular systems, where direct measurements of individual
parameters are typically hard. While Extended Kalman Filters have been used for
this purpose, the choice of the process noise covariance is generally unclear.
In this chapter, we address this issue for biomolecular systems using a
combination of Monte Carlo simulations and experimental data, exploiting the
dependence of the process noise covariance on the states and parameters, as
given in the Langevin framework. We adapt a Hybrid Extended Kalman Filtering
technique by updating the process noise covariance at each time step based on
estimates. We compare the performance of this framework with different fixed
values of process noise covariance in biomolecular system models, including an
oscillator model, as well as in experimentally measured data for a negative
transcriptional feedback circuit. We find that the Extended Kalman Filter with
such process noise covariance update is closer to the optimality condition in
the sense that the innovation sequence becomes white and in achieving a balance
between the mean square estimation error and parameter convergence time. The
results of this chapter may help in the use of Extended Kalman Filters for
systems where process noise covariance depends on states and/or parameters.Comment: 23 pages, 9 figure
A pseudo-matched filter for chaos
A matched filter maximizes the signal-to-noise ratio of a signal. In the
recent work of Corron et al. [Chaos 20, 023123 (2010)], a matched filter is
derived for the chaotic waveforms produced by a piecewise-linear system.
Motivated by these results, we describe a pseudo-matched filter, which removes
noise from the same chaotic signal. It consists of a notch filter followed by a
first-order, low-pass filter. We compare quantitatively the matched filter's
performance to that of our pseudo-matched filter using correlation functions in
a simulated radar application. On average, the pseudo-matched filter performs
with a correlation signal-to-noise ratio that is 2.0 dB below that of the
matched filter. Our pseudo-matched filter, though somewhat inferior in
comparison to the matched filter, is easily realizable at high speed (> 1 GHz)
for potential radar applications
Closed loop models for analyzing engineering requirements for simulators
A closed loop analytic model, incorporating a model for the human pilot, (namely, the optimal control model) that would allow certain simulation design tradeoffs to be evaluated quantitatively was developed. This model was applied to a realistic flight control problem. The resulting model is used to analyze both overall simulation effects and the effects of individual elements. The results show that, as compared to an ideal continuous simulation, the discrete simulation can result in significant performance and/or workload penalties
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