NASA Scientific and Technical Aerospace Reports

correlation function provides all of the information about the signal in many well-known signal processing algorithms (e.g.,
maximum entropy spectral analysis, linear predictive modeling, and Wiener filters). When a non-Gaussian time series is part
of the problem, many of the quantities used in signal processing algorithms provide inadequate information about critical
aspects of the observed data. For example, if Laplacian noise is present, the absolute error (L 1 norm) is more appropriate than
mean-square error: large deviations from the mean of the distribution a_ more likely in ‘,.he Laplacian case and should not
be weighted as much by the choice of norm. Furthermore, the correlation function captures the dependency structure of a time
series only when the time series model for it is Gaussian; only the multivariate Gaussian density depends solely on the
covariance matrix. Thus, algorithms which rely on mean-square error and the correlation function are implicitly tailored for
Gaussian signals and may not yield the best possible solutions in many other circumstances. The question becomes what
aspects of a non-Gaussian signal are required to specify its properties and how they can be used in signal processing
algorithms. A subtle, but key issue, in developing analysis tools from both theoretical and practical viewpoints is how
non-Gaussian signals are generated: given a specification of the non-Gaussian signal, how can it be produced by operations
on an elementary random sequence? Gaussian signals can be generated by passing white, Gaussian noise through the
appropriate linear system. For non-Gaussian signals, nonlinear systems are usually required, but not necessary. In this initial
study, the approach taken is to determine which measurements are necessary to specify the system which models the
generation of the signal.
Derived from text
Signal Processing; Algorithms; Maximum Entropy Method; Spectrum Analysis; Time Series Analysis; Random Noise;
Prediction Analysis Techniques
20060001676 Tampere Univ. of Technology, Finland
Suppression and Detection of Impulse Type Interference Using Adaptive Median Hybrid Filters
Nieminen, Ari; Heinomen, Pekkra; Neuvo, Yrjo; IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP ‘87); Volume 1; 1987, pp. 4.4.1-4.4.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
In this paper, we introduce a new type of nonlinear filters, the Adaptive Median Hybrid (AMH) filters, for the suppression
and detection of short duration interferences. In the AMH filters, adaptive filter substructures are used to estimate the current
signal value from the future and past signal values. The output of the overall filter is the median of the adaptive filter outputs
and the current signal value. This kind of nonlinear filter structure is shown to adapt and preserve rapid changes in signal
characteristics well. However, it filters out short duration interferences. By examining the difference between the original and
filtered data, interferences can be detected. We introduce two types of AMH filters, the AMH filter with separate adaptive
substructures (SAMH) and the AMH filter with coupled substructures (CAMH), which have different convergence properties
and implementation. We use both synthetic and real data (speech and electroencephalogram (EEG)) to show the applicability
of the proposed filters.
Author
Adaptive Filters; Electroencephalography; Nonlinear Filters; Impulses
20060001687 British Columbia Univ., Vancouver, British Columbia, Canada
A Criticism of the Parametric EEG Spike Detector
Beddoes, M. P.; Panych, L.; Qian, Juan; Wada, J. A.; IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP ‘87); Volume 1; 1987, pp. 4.5.1-4.5.4; In English; See also 20060001583
Contract(s)/Grant(s): 67-3290; Copyright; Avail.: Other Sources
The role of the parametric stage is studied under various conditions and the following points have been demonstrated: 1)
With high sampling rate (200 Hz) but otherwise favourable conditions, as the filter order, p, is increased from zero to nineteen
the signal-to-noise ratio at the output of the parametric stage remains the same as at the input. Under less favourable
conditions, it can fall as p is increased. 2) We find that comparable performance (in terms of spikes detected) is obtained when
the parametric stage is omitted entirely, and detection is based only on the very simple non-parametric stage.
Author
Electroencephalography; Signal to Noise Ratios; Signal Processing
20060001688 Massachusetts Inst. of Tech., Lexington, MA, USA
A Methodology for Evaluating the Performance of Dynamic Range Control Algorithms for Speech Enhancement
Lynch, John T.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87); Volume 1; 1987,
pp. 5.4.1-5.4.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
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