Nabeel Ali Khan

Time-frequency features for pattern recognition using high-resolution TFDs

This paper presents a tutorial review of recent advances in the field of time-frequency ( t , f ) signal processing with focus on exploiting ( t , f ) image feature information using pattern recognition techniques for detection and classification applications. This is achieved by (1) revisiting and streamlining the design of high-resolution quadratic time frequency distributions (TFDs) so as to produce adequate ( t , f ) images, (2) using image enhancement techniques to improve the resolution of TFDs, and (3) defining new ( t , f ) features such as ( t , f ) flatness and ( t , f ) entropy by extending time-domain or frequency-domain features. Comparative results indicate that the new ( t , f ) features give better performance as compared to time-only or frequency-only features for the detection of abnormalities in newborn EEG signals. Defining high-resolution TFDs for the extraction of new ( t , f ) features further improves performance. The findings are corroborated by new experimental results, theoretical derivations and conceptual insights. A streamlined methodology for designing high resolution quadratic TFDs using separable, directional and adaptive kernels.A formulation of new (t, f) features by translation from time-domain only features or frequency-domain only features.A review of (t, f) image processing techniques for resolution enhancement, de-noising and improved classification.A review of multi-component IF estimation techniques as a performance criterion to compare time-frequency distributions.Experiments that illustrate the above points in EEG seizure detection and classification using a large medical database.

Principles of time-frequency feature extraction for change detection in non-stationary signals

This paper considers the general problem of detecting change in non-stationary signals using features observed in the time-frequency (t,f) domain, obtained using a class of quadratic time-frequency distributions (QTFDs). The focus of this study is to propose a methodology to define new (t,f) features by extending time-only and frequency-only features to the joint (t,f) domain for detecting changes in non-stationary signals. The (t,f) features are used as a representative subset characterizing the status of the observed non-stationary signal. Change in the signal is then reflected as a change in the (t,f) features. This (t,f) approach is applied to the problem of detecting abnormal brain activity in newborns (e.g. seizure) using measurements of the EEG for diagnosis and prognosis. In addition, a pre-processing stage for detecting artifacts in EEG signals for signal enhancement is studied and implemented separately. Overall results indicate that, in general, the (t,f) approach results in an improved performance in detecting artifacts and seizures in newborn EEG signals as compared to time-only or frequency-only features. HighlightsWe propose (t,f) based features for detecting change in nonstationary signals.We use the features to detect seizures and artifacts in newborn EEGs.The features result in an improved performance in detecting seizures and artifacts.Performance of (t,f) features depends on the type of time-frequency distribution.

Instantaneous Frequency Estimation of Multicomponent Nonstationary Signals Using Multiview Time-Frequency Distributions Based on the Adaptive Fractional Spectrogram

This letter presents a novel algorithm to compute the instantaneous frequency (IF) of a multicomponent nonstationary signal using a combination of fractional spectrograms (FS). A high resolution time frequency distribution (TFD) is defined by combining FS computed using windows of varying lengths and chirp rates. The IF of individual signal components is then computed by applying a peak detection and component extraction procedure. The mean square error (MSE) of IF estimates computed with the AFS is lower than the MSE of IF estimates obtained from other TFDs for SNR varying from -5 dB to 16 dB.

Cross-term elimination in Wigner distribution based on 2D signal processing techniques

An efficient method based on 2D signal processing techniques and fractional Fourier transform is presented to suppress interference terms of Wigner distribution (WD). The proposed technique computes Gabor transform (GT) of a multi-component signal to obtain a blurred time-frequency (t-f) image. Signal components in GT image are segmented using connected component segmentation and are filtered out using precise application of fractional Fourier transform. A crisp t-f representation is then obtained by computing the sum of products of WD and GT of the isolated signal components. The efficacy of the proposed technique is demonstrated using examples of synthetic signals and real-life bat signals. Proposed scheme gives satisfactory performance even when cross-terms of WD overlap auto-terms and computational cost analysis shows that it is more efficient than recent interference suppression techniques of comparable performance. Moreover, the proposed technique does not require any prior info regarding the nature of signal.

A highly adaptive directional time–frequency distribution

This paper presents an automatic method of computing a high-resolution adaptive time–frequency distribution. A recently developed locally adaptive directional time–frequency distribution (ADTFD) achieves high energy concentration and cross-term suppression, but it requires manual tuning of certain parameters. One set of parameters is not applicable to all types of signals. Moreover, the ADTFD fails to achieve optimum results when a given signal has both short-duration signal components and close components. This paper overcomes the limitation of the ADTFD by locally adapting the shape of the filter. Experimental results demonstrate the efficacy of the proposed approach for a large class of signals.

Time-frequency image enhancement based on interference suppression in Wigner-Ville distribution

This paper proposes a time-frequency (t-f) image enhancement method for suppressing interference terms in the Wigner-Ville distribution. The proposed technique adapts the direction of the smoothing kernel locally at each t-f point, so that the smoothing kernel remains aligned with the ridges of the auto-terms. This local alignment of the smoothing kernel reduces cross-terms without degrading the energy concentration of auto-terms. The results indicate that the proposed time-frequency distribution outperforms other methods in terms of its ability to resolve close signal components. A new high resolution adaptive time-frequency distribution (TFD) is proposed.The proposed TFD adapts the direction of smoothing kernel on point by point basis.The proposed TFD outperforms other methods in terms of its ability to resolve close components.

Modified Gabor Wigner Transform for Crisp Time Frequency Representation

Abstract—Gabor Wigner transform (GWT) avoids the crossterms and gives high clarity in time frequency (t-f) domain.The transform is however unable to provide a clear picture insituations when auto and cross components of Wignertransform of the multi component signal overlap. This paperpresents a new simple hybrid approach that overcomes thelimitations of GWT in an efficient manner by making use offractional Fourier transform and image processing technique.Experimental results demonstrate superiority of proposedscheme.

Blind Source Separation Schemes for Mono-sensor and Multi-sensor Systems with Application to Signal Detection

In this paper, blind source separation (BSS) techniques based on time–frequency (t–f) distributions are proposed for multi-sensor and mono-sensor scenarios. The proposed schemes use t–f filtering and high-resolution t–f distributions to extract source signals that have very close components in the t–f domain. Through numerical simulations, the performance of the proposed schemes is compared with the existing algorithms. Results show that the proposed method outperforms the traditional BSS methods. In addition, the proposed BSS is applied to detect a presence of a non-stationary signal in a scenario, when noise power is unknown. Detection performance is compared with the existing detection methods through numerical simulations that show the proposed method performs better than the existing methods.

Locally Optimized Adaptive Directional Time–Frequency Distributions

This paper addresses the problem of estimating the parameters of adaptive directional time–frequency distributions (ADTFDs). ADTFDs locally optimize the direction of the smoothing kernel on the basis of directional Gaussian or double derivative directional Gaussian filter. Conventionally, the parameters of these techniques have to be tuned manually for each particular signal. Global optimization of the parameters fails to provide the desired results when the signal encompasses close or short-duration components. We propose a two-stage algorithm to resolve this issue. As part of the first stage, the length of the smoothing kernel is optimized globally. In the second stage, the parameters which control the shape of the selected smoothing window are optimized, locally. It is shown that the multistage algorithm can result in a time–frequency distribution that has both high resolution for close components and good concentration of signal energy for short-duration signal components. Experimental findings reveal the superiority of the proposed technique over the existing methods in the case of complete signals and its benefits in the case of signals with missing samples.

Improved resolution short time Fourier transform

Short time Fourier transform is simple and yet effective tool for computing time frequency representations. However, its performance greatly depends upon shape and size of window employed. Longer windows give good frequency resolution while shorter windows give good time resolution. In this paper, we present a novel method to improve the resolution of short time Fourier transform by combining short time Fourier transforms of different window lengths.