Shihab Jimaa

Multigroup classification of audio signals using time-frequency parameters

The ongoing advancements in the multimedia technologies drive the need for efficient classification of the audio signals to make the content-based retrieval process more accurate and much easier from huge databases. The challenge of this task lies in an accurate extraction of signal characteristics so as to derive a strong discriminatory feature suitable for classification. In this paper, a time-frequency (TF) approach for audio classification is proposed. Audio signals are nonstationary in nature and TF approach is the best way to analyze them. The audio signals were decomposed using an adaptive TF decomposition algorithm, and the signal decomposition parameter based on octave (scaling) was used to generate a set of 42 features over three frequency bands within the auditory range. These features were analyzed using linear discriminant functions and classified into six music groups (rock, classical, country, jazz, folk and pop). Overall classification accuracies as high as 97.6 % was achieved by linear discriminant analysis of 170 audio signals.

LTE-A an overview and future research areas

This paper gives an overview of the Long Term Evolution (LTE) of the Universal Mobile Telecommunication System (UMTS), which is being developed by the 3rd Generation Partnership Project (3GPP). LTE constitutes the latest step towards the 4th generation (4G) of radio technologies designed to increase the capacity and speed of mobile communications. Particular attention is given to the requirements and targets of LTE, its use of multiple antenna techniques, and to the Single Carrier Frequency Division Multiple Access (SC-FDMA) modulation scheme used in the LTE uplink. Furthermore new future research areas are proposed here.

Exact NLMS Algorithm with

This letter presents the exact normalized least-mean-square (NLMS) algorithm for the lp-norm-regularized square error, a popular choice for the identification of sparse systems corrupted by additive noise. The resulting exact lp-NLMS algorithm manifests differences to the original one, such as an independent update for each weight, a new sparsity-promoting compensated update, and the guarantee of stable convergence for any configuration (regardless the choice of lp norm and sparsity-tradeoff constant). Simulation results show that the exact lp-NLMS is stable and it outperforms the original one, thus validating the optimality of the proposed methodology.

{\ell _p}

This paper introduces a robust variable step size NLMS algorithm to improve interference suppression in smart antenna system. This algorithm is able to resolve signals arriving from narrowband sources propagating plane waves close to the array endfire. The results of the fixed step size NLMS will result in a trade-off issue between convergence rate and steady-state MSE of NLMS algorithm. This issue is solved by changing the step size from constant to variable. The proposed VSSNLMS algorithm reduces the mean square error (MSE) and shows faster convergence rate when compared to the conventional NLMS. Moreover, the enhanced performance of the new VSSNLMS algorithm is validated from the output beam pattern which introduces deeper nulls to mitigate the effect of interfering signals.

-Norm Constraint

Embedding a sparse penalty in conventional Least Mean Square (LMS) adaptive algorithms is an established strategy to enhance the performance and robustness against noise in the estimation of sparse plants, such as wireless mul-tipath channels. In this paper we review the most prominent NLMS-based algorithms with ℓp-norm constraint, discussing the underlying mechanisms that lead to improvement gains in sparse scenarios. Simulation results validate the analysis and comparative discussion. Given that adaptive algorithms operating in time domain deteriorate with correlated signals, we propose hereby a novel frequency-domain (FD) ℓp-NLMS that performs in such situations. Simulation results indicate that the proposed method outperforms its time-domain counterparts not only in convergence rate but more importantly in residual misalignment. This important result has not been echoed so far.

Robust interference suppression using a new LMS-based adaptive beamforming algorithm

Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM), MIMO-OFDM, is considered as a spectrally efficient approach to achieve high throughput communications. This paper investigates the performance of MIMO-OFDM system using the proposed NLMS adaptive equalizer and the developed Singular Value Decomposition (SVD) technique in estimating the channel. The performances of using various values of the NLMS algorithms step-size were also investigated and an optimum value, based on a trade-off between the convergence speed and the steady state noise floor, was chosen. Then, the bit error rate (BER) performance of the NLMS adaptive receiver is compared with that of the SVD algorithm. It is clear that the SVD gives better performance over the NLMS adaptive filter.

Sparse NLMS adaptive algorithms for multipath wireless channel estimation

Embedding the norm in gradient-based adaptive filtering is a popular solution for sparse plant estimation. Even though the foundations are well understood, the selection of the sparsity hyper-parameter still remains today matter of study. Supported on the modal analysis of the adaptive algorithm near steady state, this paper shows that the optimal sparsity tradeoff depends on filter length, plant sparsity and signal-to-noise ratio. In a practical implementation, these terms are obtained with an unsupervised mechanism tracking the filter weights. Simulation results prove the robustness and superiority of the novel adaptive-tradeoff sparsity-aware method.

Enhanced channel estimation technique in MIMO-OFDM system

The ℓ0-normalized least mean squares (ℓ0-NLMS) is arguably the reference gradient adaptive algorithm for sparse system estimation. However, alike all sparse gradient adaptive algorithms, the ℓ0-NLMS performance is sensitive to the (adequate) selection of the tradeoff parameter. Highlighted in this letter, the existence of two convergence modes, linked to the negligible and to the significant taps, paves the way for the convergence analysis, which results in a set of nonlinear (quadratic) convergence equations. Therefrom, the minimization of the steady-state misalignment concludes in the optimal tradeoff, which happens to relate to the NLMS step size, filter length, plant sparsity, and noise level in an extremely compact fashion. Exhaustive simulation experiments show strong agreement between the analytical predictions and the empirical performance.

Adaptive sparsity tradeoff for ℓ1-constraint NLMS algorithm

The use of two simple and robust variable step-size approaches in the adaptation process of the Normalized Least Mean Square (NLMS) algorithm (VSS-NLMS) in the adaptive channel equalization is investigated. The NLMS algorithm with a fixed step-size (FSS-NLMS) usually results in a trade-off between the residual error and the convergence speed of the algorithm. It is proved by computer simulation that the VSS-NLMS algorithms presented here eliminate much of this trade-off. In this paper the Mean-Square Error (MSE) performance of using the VSS-NLMS algorithms in the adaptation process of adaptive channel equalization is investigated. The step-size variation makes it possible for the VSS-NLMS algorithm to converge faster and to a lower steady state error than in the FSS-NLMS case.

Optimal Sparsity Tradeoff in

The ongoing advancements in the multimedia technologies drive the need for efficient classification of the audio signals to make the content-based retrieval process more accurate and much easier from huge databases. The challenge of this task lies in an accurate extraction of signal characteristics so as to derive a strong discriminatory feature suitable for retrieval process. A time-frequency approach for audio classification is proposed. The audio signals were decomposed using an adaptive time-frequency decomposition algorithm, and the signal decomposition parameter octave (scale) was used to create patterns based on a similarity measure of the audio signals. These patterns were used to generate templates to classify the audio signals into different categories. Initial studies have yielded a overall correct classification accuracy of 90% with a database of 64 audio segments.