Abdelkhalek Bouchikhi

Speech Enhancement via EMD

In this study, two new approaches for speech signal noise reduction based on the empirical mode decomposition (EMD) recently introduced by Huang et al. (1998) are proposed. Based on the EMD, both reduction schemes are fully data-driven approaches. Noisy signal is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs), using a temporal decomposition called sifting process. Two strategies for noise reduction are proposed: filtering and thresholding. The basic principle of these two methods is the signal reconstruction with IMFs previously filtered, using the minimum mean-squared error (MMSE) filter introduced by I. Y. Soon et al. (1998), or thresholded using a shrinkage function. The performance of these methods is analyzed and compared with those of the MMSE filter and wavelet shrinkage. The study is limited to signals corrupted by additive white Gaussian noise. The obtained results show that the proposed denoising schemes perform better than the MMSE filter and wavelet approach.

Multicomponent AM-FM signals analysis based on EMD-B-splines ESA

In this paper a signal analysis framework for estimating time-varying amplitude and frequency functions of multicomponent amplitude and frequency modulated (AM-FM) signals is introduced. This framework is based on local and non-linear approaches, namely Energy Separation Algorithm (ESA) and Empirical Mode Decomposition (EMD). Conjunction of Discrete ESA (DESA) and EMD is called EMD-DESA. A new modified version of EMD where smoothing instead of an interpolation to construct the upper and lower envelopes of the signal is introduced. Since extracted IMFs are represented in terms of B-spline (BS) expansions, a closed formula of ESA robust against noise is used. Instantaneous Frequency (IF) and Instantaneous Amplitude (IA) estimates of a multicomponent AM-FM signal, corrupted with additive white Gaussian noise of varying SNRs, are analyzed and results compared to ESA, DESA and Hilbert transform-based algorithms. SNR and MSE are used as figures of merit. Regularized BS version of EMD-ESA performs reasonably better in separating IA and IF components compared to the other methods from low to high SNR. Overall, obtained results illustrate the effectiveness of the proposed approach in terms of accuracy and robustness against noise to track IF and IA features of a multicomponent AM-FM signal.

Analysis of multicomponent LFM signals by Teager Huang-Hough transform

A novel detection approach of linear FM (LFM) signals, with single or multiple components, in the time-frequency plane of Teager-Huang (TH) transform is presented. The detection scheme that combines TH transform and Hough transform is referred to as Teager-Huang-Hough (THH) transform. The input signal is mapped into the time-frequency plane by using TH transform followed by the application of Hough transform to recognize time-frequency components. LFM components are detected and their parameters are estimated from peaks and their locations in the Hough space. Advantages of THH transform over Hough transform of Wigner-Ville distribution (WVD) are: 1) cross-terms free detection and estimation, and 2) good time and frequency resolutions. No assumptions are made about the number of components of the LFM signals and their models. THH transform is illustrated on multicomponent LFM signals in free and noisy environments and the results compared with WVD-Hough and pseudo-WVD-Hough transforms.

Speech signal noise reduction by EMD

In this paper, a speech signal noise reduction based on a multiresolution approach referred to as Empirical Mode Decomposition (EMD) [1] is introduced. The proposed speech denoising method is a fully data-driven approach. Noisy signal is decomposed adaptively into oscillatory components called Intrinsic Mode Functions (IMFs), using a temporal decomposition called sifting process. The basic principle of the method is to reconstruct the signal with IMFs previously thresholded using a shrinkage function. The denoising method is applied to speech with different noise levels and the results are compared to wavelet shrinkage. The study is limited to signals corrupted by additive white Gaussian noise.

Empirical Mode Decomposition and some operators to estimate Instantaneous Frequency: A comparative study

The aim of the present work is to illustrate how the empirical mode decomposition (EMD) can be associated with demodulation methods to estimate the instantaneous frequency (IF) of multicomponent non stationary signals. The IF estimation of three methods, namely, Hilbert-Huang transform (HHT) [10], EMD-DESA (Discrete Energy Separation Algorithm)also known as Teager-Huang transform (THT) [2] [5], and B-spline version of the EMD-DESA called EMD-ESA-BS [1] are compared on AM-FM signal corrupted with an additive white Gaussian noise of varying signal to noise ratio (SNR). The obtained results show that for very low SNR the EMD-ESA-BS with regularization performs better than the HHT, the THT and the EMD-ESA- BS without regularization. For high SNR values all the methods globally have the same behavior, but the EMD-ESA-BS without regularization gives the best estimation.

A combined Teager-Huang and Hough Transforms for LFM signals detection

A new method for linear FM (LFM) signals detection in the time-frequency plane using Teager-Huang Transform (THT) is proposed. Time-Frequency Representation (TFR) is viewed as an image where image processing techniques are applied to detect frequency patterns of interest. THT is used in conjunction with Hough Transform (HgT) called (THHT), where the output is a TFR free of cross-terms. THHT is applied to signals composed of LFM and the results are compared to Wigner-Ville Distribution-HgT and smoothed Wigner-Ville Distribution-HgT. Results show the good performance of the THHT in terms of detection and estimation compared to WVD based methods.

On the detection of transient signals using cross-Ψ B -energy operator

In this paper we point out new properties, composition, wideband filtering and quadratic principle, of the cross-PsiB-energy operator which measures the interaction between two non- stationary signals. These properties are useful for some signal processing applications. We show that due to its bilinearity, PsiB satisfies the quadratic superposition principle that can be used efficiently for transient signals detection purpose. Results on transient signals detection are presented and discussed.

An imroved image demodulation algorithm based on Teager-Kaiser operator

In this paper a new wideband image demodulation algorithm based on the Teager-Kaiser operator is proposed. A Gabor filter bank is first used to decompose the wideband image into multiple narrow band components. Directly applied on these components, the demodulation yields many artifacts on both the amplitude and the frequency modulation counterparts. To reduce these artifacts, a low-pass filter in the demodulation process is introduced. An approximation of the Teager-Kaiser filtered image energy is proposed. A proposition related to that fact, along with the associated proof are provided. As illustrated by various examples, our demodulation approach is more robust to noise, and drastically improves the visual quality of the demodulation results. A direct consequence post-filtering or/and post-processing are not necessary.