The bootstrap and its application in signal processing

The bootstrap is an attractive tool for assessing the accuracy of estimators and testing hypothesis for parameters where conventional techniques are not valid, such as in small data-sample situations. We highlight the motivations for using the bootstrap in typical signal processing applications and give several practical examples. Bootstrap methods for testing statistical hypotheses are described and we provide an analysis of the accuracy of bootstrap tests. We also discuss how the bootstrap can be used to estimate a variance-stabilizing transformation to define a pivotal statistic, and we demonstrate the use of the bootstrap for constructing confidence intervals for flight parameters in a passive acoustic emission problem     

The development of a new signal processing program at theQueensland University of Technology

The School of Electrical and Electronic Systems Engineering at Queensland University of Technology, Brisbane, Australia (QUT), offers three bachelor degree courses in electrical and computer engineering. In all its courses there is a strong emphasis on signal processing. A newly established Signal Processing Research Center (SPRC) has played an important role in the development of the signal processing units in these courses. This paper describes the unique design of the undergraduate program in signal processing at QUT, the laboratories developed to support it, and the criteria that influenced the design     

Comments on “The Cramer-Rao lower bounds for signals withconstant amplitude and polynomial phase”

For original paper see IEEE Trans. Signal Processing, vol.39, p.749-52 (March 1991). Different expressions for the Cramer-Rao lower bounds (CRLBs) of constant amplitude polynomial phase signals embedded in white Gaussian noise appear in the literature. The present paper revisits the derivation of the bounds reported by Peleg and Porat (1991) and indicates that the resulting expressions depend on the interval over which the signal is defined. The proper choice of the interval is the one that centers the signal around zero and results in the minimum lower bounds     

Wigner-Ville analysis of asymptotic signals and applications

The Wigner-Ville joint representation (WVJR) appears as a quite good candidate among the various time and frequency JR. This JR performs exact demodulation of finite energy signals even in presence of linear filtering. Signal or waveform synthesis may be derived from this JR or from ambiguity function. Asymptotic signals are depicted in such a way that modulation parameters may be estimated. Dealing with vibroseismic signals the WVJR allows to identify damping parameters related to propagation effects. Propagation models may be checked by this technique. An example of such a technique is provided by processing of synthetic seismic data.     

A high-resolution quadratic time-frequency distribution formulticomponent signals analysis

The paper introduces a new kernel for the design of a high resolution time-frequency distribution (TFD). We show that this distribution can solve problems that the Wigner-Ville distribution (WVD) or the spectrogram cannot. In particular, the proposed distribution can resolve two close signals in the time-frequency domain that the two other distributions cannot. Moreover, we show that the proposed distribution is more accurate than the WVD and the spectrogram in the estimation of the instantaneous frequency of a stepped FM signal embedded in additive Gaussian noise. Synthetic and real data collected from real-world applications are shown to validate the proposed distribution     

A time-delay digital tanlock loop

We propose a nonuniform sampling digital tanlock loop (DTL) that utilizes a constant time-delay unit instead of the constant 90° phase shifter The new structure reduces the complexity of implementation and avoids many of the practical problems associated with the digital Hilbert transformer like the approximations and frequency limitations. The time-delay digital tanlock loop (TDTL) preserves the most important features of the conventional DTL (CDTL), such as reduced sensitivity to the variation of the signal power. It also introduces improvement over the first-order CDTL under suitable choice of the circuit parameters. The first- and second-order loops are analyzed for locking conditions and steady-state phase error     

Interpolation of pitch contour using temporal decomposition

A new method for predicting pitch contour of a speech signal using a small number of pitch values is addressed, for the application of very low rate speech coding, relying on the correlation between phonetic evolution and pitch variations during voiced speech segments. To track the phonetic evolution and specify perceptually significant time points, Temporal Decomposition (TD) is used. TD provides information required for both determination of critical pitch values and estimation of pitch contour by detecting event functions, as interpolation paths, and their centroids, as the most steady points, in the spectral parameters space. It is shown that the proposed method reduces the amount of pitch information to about one-tenth of that in conventional frame-by-frame based techniques with less than 5% error in pitch approximation.     

Hierarchical approach to formant detection and tracking throughinstantaneous frequency estimation

Formant frequencies, represented by major peaks in the spectrum of speech signals, convey important information about speech. The authors propose a method for detecting the formants of voiced speech through `instantaneous frequency' (IF) estimation using a recursive least square (RLS) algorithm. The accuracy of the technique is assessed by comparing it with conventional formant detection techniques. This method is also analysed from the viewpoint of phonetic conformity using `temporal decomposition'     

Seizure detection of newborn EEG using a model-based approach

Seizures are often the first sign of neurological disease or dysfunction in the newborn. However, their clinical manifestation is often subtle, which tends to hinder their diagnosis at the earliest possible time. This represents an undesirable situation since the failure to quickly and accurately diagnose seizure can lead to longer-term brain injury or even death. In this paper we consider the problem of automatic seizure detection in the neonate based on electroencephalogram (EEG) data. We propose a new approach based on a model for the generation of the EEG, which is derived from the histology and biophysics of a localized portion of the brain. We show that by using this approach, good detection performance of electrographic seizure is possible. The model for seizure is first presented along with an estimator for the model parameters. Then we present a seizure-detection scheme based on the model parameter estimates. This scheme is compared with the quadratic detection filter (QDF), and is shown to give superior performance over the latter. This is due to the ability of the model-based detector to account for the variability (nonstationarity) of the EEG by adjusting its parameters appropriately.     

Adaptive instantaneous frequency estimation of multicomponent FMsignals using quadratic time-frequency distributions

An adaptive approach to the estimation of the instantaneous frequency (IF) of nonstationary mono- and multicomponent FM signals with additive Gaussian noise is presented. The IF estimation is based on the fact that quadratic time-frequency distributions (TFDs) have maxima around the IF law of the signal. It is shown that the bias and variance of the IF estimate are functions of the lag window length. If there is a bias-variance tradeoff, then the optimal window length for this tradeoff depends on the unknown IF law. Hence, an adaptive algorithm with a time-varying and data-driven window length is needed. The adaptive algorithm can utilize any quadratic TFD that satisfies the following three conditions: First, the IF estimation variance given by the chosen distribution should be a continuously decreasing function of the window length, whereas the bias should be continuously increasing so that the algorithm will converge at the optimal window length for the bias-variance tradeoff, second, the time-lag kernel filter of the chosen distribution should not perform narrowband filtering in the lag direction in order to not interfere with the adaptive window in that direction; third, the distribution should perform effective cross-terms reduction while keeping high resolution in order to be efficient for multicomponent signals. A quadratic distribution with high resolution, effective cross-terms reduction and no lag filtering is proposed. The algorithm estimates multiple IF laws by using a tracking algorithm for the signal components and utilizing the property that the proposed distribution enables nonparametric component amplitude estimation. An extension of the proposed TFD consisting of the use of time-only kernels for adaptive IF estimation is also proposed