Amro El-Jaroudi

Discrete all-pole modeling

A method for parametric modeling and spectral envelopes when only a discrete set of spectral points is given is introduced. This method, called discrete all-pole (DAP) modeling, uses a discrete version of the Itakura-Saito distortion measure as its error criterion. One result is an autocorrelation matching condition that overcomes the limitations of linear prediction and produces better fitting spectral envelopes for spectra that are representable by a relatively small discrete set of values, such as in voiced speech. An iterative algorithm for DAP modeling that is shown to converge to a unique global minimum is presented. Results of applying DAP modeling to real and synthetic speech are also presented. DAP modeling is extended to allow frequency-dependent weighting of the error measure, so that spectral accuracy can be enhanced in certain frequency regions. >

Evolutionary periodogram for nonstationary signals

Presents a novel estimator for the time-dependent spectrum of a nonstationary signal. By modeling the signal, at any given frequency, as having a time-varying amplitude accurately represented by an orthonormal basis expansion, the authors are able to compute a minimum mean-squared error estimate of this time-varying amplitude. Repeating the process over all frequencies, they obtain a power distribution as a function of time and frequency that is consistent with the Wold-Cramer evolutionary spectrum. Based on the model assumptions, the authors develop the evolutionary periodogram (EP) for nonstationary signals, an estimator analogous to the periodogram used in the stationary case. They also derive the time-frequency resolution of the new estimator. The approach is free of some of the drawbacks of the bilinear distributions and of the short-time Fourier transform spectral estimates. It is guaranteed to produce nonnegative spectra without the cross-term behavior of the bilinear distributions, and it does not require windowing of data in the time domain. Examples illustrating the new estimator are given. >

A new error criterion for posterior probability estimation with neural nets

The authors introduce an error criterion for training which improves the performance of neural nets as posterior probability estimators, as compared to using least squares. The proposed criterion is similar to the Kullback-Leibler information measure and is simple to use. A straightforward iterative algorithm for the minimization of the error criterion which has been shown to have good convergence properties is described. The authors applied the proposed technique to some classification examples and showed it to produce better posterior probability estimates than least squares, especially for low probabilities

Data-adaptive evolutionary spectral estimation

We present a novel data-adaptive estimator for the evolutionary spectrum of nonstationary signals. We model the signal at a frequency of interest as a sinusoid with a time-varying amplitude, which is accurately represented by an orthonormal basis expansion. We then compute a minimum mean-squared error estimate of this amplitude and use it to estimate the time-varying spectrum at that frequency, all while minimizing the interference from the signal components at other frequencies. Repeating the process over all frequencies, we obtain a power distribution that is consistent with the Wold-Cramer evolutionary spectrum and reduces to Capons (1969) method for the stationary case. Our estimator possesses desirable properties in terms of time-frequency resolution and positivity and is robust in the spectral estimation of noisy nonstationary data. We also propose a new estimator for the autocorrelation of nonstationary signals. This autocorrelation estimate is needed in the data-adaptive spectral estimation. We illustrate the performance of our estimator using simulation examples and compare it with the recently presented evolutionary periodogram and the bilinear time-frequency distribution with exponential kernels. >

Speech signal modification to increase intelligibility in noisy environments

The role of transient speech components on speech intelligibility was investigated. Speech was decomposed into two components--quasi-steady-state (QSS) and transient--using a set of time-varying filters whose center frequencies and bandwidths were controlled to identify the strongest formant components in speech. The relative energy and intelligibility of the QSS and transient components were compared to original speech. Most of the speech energy was in the QSS component, but this component had low intelligibility. The transient component had much lower energy but was almost as intelligible as the original speech, suggesting that the transient component included speech elements important to speech perception. A modified version of speech was produced by amplifying the transient component and recombining it with the original speech. The intelligibility of the modified speech in background noise was compared to that of the original speech, using a psychoacoustic procedure based on the modified rhyme protocol. Word recognition rates for the modified speech were significantly higher at low signal-to-noise ratios (SNRs), with minimal effect on intelligibility at higher SNRs. These results suggest that amplification of transient information may improve the intelligibility of speech in noise and that this improvement is more effective in severe noise conditions.

Power spectral analysis of EEG in a multiple-bedroom, multiple-polygraph sleep laboratory

OBJECTIVES We describe the methods for power spectral analysis (PSA) of sleep electroencephalogram (EEG) data at a large clinical and research sleep laboratory. The multiple-bedroom, multiple-polygraph design of the sleep laboratory poses unique challenges for the quantitative analysis of the data. This paper focuses on the steps taken to ensure that our PSA results are not biased by the particular bedroom or polygraph from which the data were acquired. METHODS After describing the data acquisition system hardware, we present our signal amplitude calibration procedure and our methods for performing PSA. We validate the amplitude calibration procedure in several experiments using PSA to establish tolerances for data acquisition from multiple bedrooms and polygraphs. RESULTS Since it is not possible to acquire identical digitized versions of an EEG signal using different sets of equipment, the best that can be achieved is data acquisition that is polygraph-independent within a known tolerance. We are able to demonstrate a tolerance in signal amplitude of +/- 0.25% when digitizing data from different bedrooms. When different data acquisition hardware is used, the power tolerance is approximately +/- 3% for frequencies from 1 to 35 Hz. The power tolerance is between +/- 3 and +/- 7% for frequencies below 1 Hz and frequencies between 35 and 50 Hz. Additional data demonstrate that variability due to the hardware system is small relative to the inherent variability of the sleep EEG. CONCLUSION The PSA results obtained in one location can be replicated elsewhere (subject to known tolerances) only if the data acquisition system and PSA method are adequately specified.

Partitioning capabilities of two-layer neural networks

It has been observed that feedforward neural nets with a single hidden layer are capable of forming either convex decision regions or nonconvex but connected decision regions in the input space. In this correspondence, it is shown that two-layer nets with a single hidden layer are capable of forming disconnected decision regions as well. In addition to giving examples of the phenomenon, it is explained why and how disconnected decision regions are formed. Through the hypothesization of the existence of additional virtual cells formed by the first layer, it is shown how the decision regions formed by the second layer can indeed be disconnected. It is shown that the number of such disconnected regions can be very large. Using a recent theoretical result about the sufficiency of two layers to approximate arbitrary decision regions in a finite portion of the space, an example is given of how that is possible with the use of virtual cells. >

Iterative instantaneous frequency estimation and adaptive matched spectrogram

Abstract In this paper, we present an iterative algorithm to estimate the instantaneous frequency (IF) and matched spectrogram of nonstationary signals. The matched spectrogram obtained by this method is concentrated along the IF for monocomponent signals. The proposed algorithm is then combined with a simple window adaptation scheme to obtain an adaptive spectrogram method matched to the estimated IF. The convergence analysis and the properties of the IF estimation algorithm are presented. Finally examples showing the performance of the proposed algorithms are given.

Informative priors for minimum cross-entropy positive time-frequency distributions

A method for generating an informative prior when constructing a positive time-frequency distribution (TFD) by the method of the minimum cross-entropy (MCE) is developed. The prior is obtained from a combination of the Wigner distribution (WD) and the evolutionary periodogram, and results in a more informative MCE-TFD, as quantified via the mutual information of the distribution. The procedure allows any of the bilinear distributions to be used in the prior. Examples illustrate the performance of the new technique.

Classification capabilities of two-layer neural nets

The authors consider the classification capabilities of feedforward two-layer neural nets with a single hidden layer and having threshold units only; that is they consider the type of decision regions that two-layer nets are capable of forming in the input space. It had been asserted previously that such nets are capable of forming only convex decision regions or nonconvex but connected regions. The authors show that two-layer nets are capable of forming disconnected decision regions as well. In addition to giving examples of the phenomena, they explain why and how disconnected decision regions are formed. They also derive an expression for the number of cells in the input space that are to be grouped together to form the decision regions. This expression can be useful in deciding how many nodes to have in the first layer. The results have bearing on neural networks where the nonlinear elements are smooth (sigmoid) functions rather than threshold functions.<<ETX>>