MONIA ALOUANE - Academia.edu (original) (raw)

Papers by MONIA ALOUANE

Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland, 2009

Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 2007

2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), 2017

In this paper, we introduce a perceptual algorithm, called Perceptual Orthogonal Matching Pursuit... more In this paper, we introduce a perceptual algorithm, called Perceptual Orthogonal Matching Pursuit (POMP), for efficient sparse modeling of wideband speech signals. As its name suggests, POMP is basically built upon the well-known Orthogonal Matching Pursuit (OMP) but differs from it in that it accounts for the human hearing properties. The perceptual component within POMP algorithm is represented by the perceptual weighting filter embedded into the distortion measure. Through a series of locally optimal updates, this measure is aimed at being minimized. POMP has the advantage of being able to handle any real dictionary, as opposed to state of the art perceptual algorithms conceived to handle sinusoidal dictionaries exclusively. We exemplify the application of the algorithm to sparse modeling of wideband speech signals using predefined and adaptive dictionaries. Simulation results show that POMP outperforms OMP and provides a significant improvement of atom selection over the conside...

Interspeech 2015, 2015

Listeners suffering from presbycusis (age-related hearing loss) often report difficulties when at... more Listeners suffering from presbycusis (age-related hearing loss) often report difficulties when attempting to understand vocal announcements in public spaces. Current solutions that improve speech intelligibility for hearing-impaired subjects mainly consist of customized solutions, such as hearing aids. This study proposes a more generic strategy, which would enhance speech perception for both normal-hearing and hearing-impaired listeners, i.e. For All. It provides the early stages of such an approach. Digital filters with different degrees of hearing-loss compensation have been designed, getting inspired by the way hearing aids process speech signals. Subjective tests conducted on normal-hearing and presbycusis subjects confirmed that it is possible to improve speech intelligibility for both types of population simultaneously.

Multimedia Tools and Applications, 2019

In the context of data embedding in audio for communications, Spread Spectrum (SS)-based techniqu... more In the context of data embedding in audio for communications, Spread Spectrum (SS)-based techniques, combined with auditory models, are efficient in terms of robustness and perceptual quality of the modified host audio. However, their main drawback is the limited embedding capacity due to the strong interference caused by the host audio. In this work, we combine under-determined Blind Source Separation with a generic SS receiver-made of a Wiener equalizer and a correlation-based demodulator-to efficiently reduce the effect of the host interference. Two blocks are added to the generic receiver: an Under-determined Independent Subspace Analysis (UISA) block is placed after the Wiener equalizer in order to separate the components of the equalized output, and a cooperative detection block is applied to the UISA outputs in order to extract the relevant information from all available components. The UISA block uses Empirical Mode Decomposition to obtain multiple observations of the modified host signal. The performance of the proposed system in terms of bit error rate vs. bit-rate is significantly improved: average error rate is null for bit-rates below 500 bps and smaller than 0.1% for bit-rates reaching 1 kbps. The objective evaluation of the perceptual quality of the modified audio confirms the imperceptibility of the embedded data. The proposed system also exhibits robustness against common signal processing operations such as gain modification, noise, MPEG compression and re-quantization.

2015 23rd European Signal Processing Conference (EUSIPCO), 2015

In a compressive sensing context, we propose a solution for a full learning of the dictionary com... more In a compressive sensing context, we propose a solution for a full learning of the dictionary composed of the sparsity basis and the measurement matrix. The sparsity basis learning process is achieved using Empirical Mode Decomposition (EMD) and Hilbert transformation. EMD being a data-driven decomposition method, the resulting sparsity basis shows high sparsifying capacities. On the other hand, a gradient method is applied for the design of the measurement matrix. The method integrates the dictionary normalization into the target function. It is shown to support large scale problems and to have a good convergence and high performance. The evaluation of the whole approach is done on a set of environmental sounds, and is based on a couple of key criteria: sparsity degree and incoherence. Experimental results demonstrate that our approach achieves well with regards to mutual coherence reduction and signal reconstruction at low sparsity degrees.

IET Signal Processing, 2016

This study presents a speech filtering method exploiting the combined effects of the empirical mo... more This study presents a speech filtering method exploiting the combined effects of the empirical mode decomposition (EMD) and the local statistics of the speech signal using the adaptive centre weighted average (ACWA) filter. The novelty lies in incorporating the frame class (voiced/unvoiced) in the conventional filtering using the EMD and the ACWA filter. The speech signal is segmented into frames and each one is broken down by the EMD into a finite number of intrinsic mode functions (IMFs). The number of filtered IMFs depends on whether the frame is voiced or unvoiced. An energy criterion is used to identify voiced frames while a stationarity index distinguishes between unvoiced and transient sequences. Reported results obtained on signals corrupted by additive noise (white, F16, factory) show that the proposed filtering in line with the frame class is very effective in removing noise components from noisy speech signal. Compared with filtering results of the wavelet, the ACWA, and the EMD-ACWA methods, the proposed technique gives much better results in terms of average segmental signal-to-noise ratio and listening quality based on perceptual evaluation speech quality score.

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014

Nonlinear audio system identification generally relies on Gaussianity, whiteness and stationarity... more Nonlinear audio system identification generally relies on Gaussianity, whiteness and stationarity hypothesis on the input signal, although audio signals are non-Gaussian, highly correlated and nonstationary. However, since the physical behavior of nonlinear audio systems is input-dependent, they should be identified using natural audio signals (speech or music) as input, instead of artificial signals (sweeps or noise) as usually done. We propose an identification scheme that conditions audio signals to fit the desired properties for an efficient identification. The identification system consists in (1) a Gaussianization step that makes the signal near-Gaussian under a perceptual constraint; (2) a predictor filterbank that whitens the signal; (3) an orthonormalization step that enhances the statistical properties of the input vector of the last step, under a Gaussianity hypothesis; (4) an adaptive nonlinear model. The proposed scheme enhances the convergence rate of the identification and reduces the steady state identification error, compared to other schemes, for example the classical adaptive nonlinear identification.

Fourth International Conference on Communications and Networking, ComNet-2014, 2014

This paper deals with particle filters (PF) based on quantized innovation for distributed estimat... more This paper deals with particle filters (PF) based on quantized innovation for distributed estimation (DE) in ad hoc wireless sensor network (WSN) within a robot tracking context. When only one bit is exchanged per instant between sensors the sign of innovation particle filter (SOI-PF) has a good tracking ability. However, we propose in this work to show that processing of more than one SOI information coming from a set of neighboring sensors, simultaneously, is more performant than using a multi level quantized innovation on the same number of bits. The tracking performance of the so resulted multi-SOI PF, presented here in the context of nonlinear robot's motion tracking, are compared then to the multi level quantized innovation PF (MLQI-PF), which is derived in this work for a nonlinear and non gaussian tracking context. Simulations show that the MSOI-PF algorithm outperforms the MLQI-PF, moreover, the MSOI-PF's performance with only two binary information are similar to that of the PF based on a non quantized innovation.

2008 IEEE 9th Workshop on Signal Processing Advances in Wireless Communications, 2008

Distributed estimation is a major feature in wireless sensor networks (WSNs). Recently, hard quan... more Distributed estimation is a major feature in wireless sensor networks (WSNs). Recently, hard quantized observations based on sign of innovation (SOI) were used to perform optimal distributed filtering involving thus the SOI Kalman filter (KF)/extended KF (EKF) [1]. In this paper, a SOI-particle filter (SOIPF) is derived to enhance the performance of the distributed estimation procedure. On one hand, the

2009 Third International Conference on Sensor Technologies and Applications, 2009

AbstractThis paper deals with target tracking in a binary wireless sensor network. In this contr... more AbstractThis paper deals with target tracking in a binary wireless sensor network. In this contribution, the target motion is represented by a non linear model based on a jump-Markovian direction. The observation of the trajectory based on the target signal strength is ...

2012 IEEE 75th Vehicular Technology Conference (VTC Spring), 2012

This paper 1 presents a novel technique in spectrum sensing based on a new characterization of pr... more This paper 1 presents a novel technique in spectrum sensing based on a new characterization of primary users signals in wideband communications. First, we have to remind that in cognitive radio networks, the very first task to be operated by a cognitive radio is sensing and identification of spectrum holes in the wireless environment. This paper summarizes the advances in the algebraic approach. Initial results have been already disseminated in few other conferences. This paper aims at finalizing and presenting the last results and the complete framework of the proposed technique based on algebraic spectrum discontinuities detection. The signal spectrum over a wide frequency band is decomposed into elementary building blocks of subbands that are well characterized by local irregularities in frequency. As a powerful mathematical tool for analyzing singularities and edges, the algebraic framework is employed to detect and estimate the local spectral irregular structure, which carries important information on the frequency locations and power spectral densities of the sensed subbands. In this context, a wideband spectrum sensing techniques was developed based on an analog decision function to multi-scale wavelet product. The proposed sensing techniques provide an effective sensing framework to identify and locate spectrum holes in the signal spectrum.

The Second International Conference on Communications and Networking, 2010

This paper deals with target tracking in a binary Wireless Sensor Network (WSN) context. In parti... more This paper deals with target tracking in a binary Wireless Sensor Network (WSN) context. In particular, we propose to study the performance of the Sign Of Innovation Particle Filter (SOI-PF) algorithm in a noisy context. This parallel algorithm based on the exchange of only one bit by instant between the sensors, has shown its efficiency for target tracking in a

2010 5th International Symposium On I/V Communications and Mobile Network, 2010

This paper deals with pre-echo reduction in low bit-rate audio compression. [1] proposed an attac... more This paper deals with pre-echo reduction in low bit-rate audio compression. [1] proposed an attack restoration method based on the correction of the temporal envelop of the decoded signal. A small set of coefficients were then transmitted through a limited bit-rate auxiliary channel. However, the transmission of the transient position computed on the original audio signal was required. In this paper, we deployed a new method of attack localization based on differential algebraic, which guaranties a successful detection on the decoded audio signal. The algebraic method has also a reduced complexity compared to the index stationary detector used in [1]. The new proposed approach is evaluated for single audio coding-decoding, using objective perceptual measures. The experimental results for MP3 coding exhibits an efficient restoration of the attacks and a significant improvement of the audio quality.

Proceedings of the 7th International Conference on Cognitive Radio Oriented Wireless Networks, 2012

This paper 1 deals with spectrum sensing techniques used to efficient utilization of limited spec... more This paper 1 deals with spectrum sensing techniques used to efficient utilization of limited spectrum resource. The most used technique is the energy detector as it is the simplest one for real time implementation and performs well for high SNR. This paper is concerned with this well known method and introduces an enhanced energy detector, in order to enhance its performance in low SNR. The proposed detector is based on the algebraic approach used for spike location, to make the detection more robust in a noisy environment. The proposed system model introduces an algebraic preprocessing bloc to attenuate the noise effect and enhance the detector performance at lower SNR. The detection is achieved by a second bloc that implements the conventional energy detection method. Simulation results, performed on DVB-T signals in additive Gaussian noisy context, show that the proposed detector performs much better than the conventional energy detector.

EURASIP Journal on Wireless Communications and Networking, 2012

This article presents a spectrum sensing algorithm for wideband cognitive radio exploiting sensed... more This article presents a spectrum sensing algorithm for wideband cognitive radio exploiting sensed spectrum discontinuity properties. Some work has already been investigated by wavelet approach by Giannakis et al., but in this article we investigate an algebraic framework in order to model spectrum discontinuities. The information derived at the level of these irregularities will be exploited in order to derive a spectrum sensing algorithm. The numerical simulation show satisfying results in terms of detection performance and receiver operating characteristics curves as the detector takes into account noise annihilation in its inner structure.

2008 2nd International Conference on Signals, Circuits and Systems, 2008

This paper introduces a new speech enhancement method, which combines adaptive center weighted av... more This paper introduces a new speech enhancement method, which combines adaptive center weighted average (ACWA) filter with empirical mode decomposition (EMD). Both ACWA and EMD operate in the time domain. The ACWA filter is advantageous as it operates adaptively in the time domain and does not require the stationarity and the whiteness of the signals. Thanks to the data driven decomposition of the EMD, the application of the ACWA filter on the IMFs gives better results than the ACWA filtering of the noisy signal. The proposed EMD-ACWA denoising method is applied to noisy speech signal with different noise levels and the results are compared to those obtained by two different denoising methods: wavelet thresholds and ACWA filtering. A significant superiority of the EMD-ACWA method over the two others is shown in white noisy contexts as well as in correlated noisy ones.

2008 3rd International Symposium on Communications, Control and Signal Processing, 2008

Kais KHALDI ∗, Abdel-Ouahab BOUDRAA , Abdelkhalek BOUCHIKHI , ... Monia TURKI-HADJ ALOUANE ... more Kais KHALDI ∗, Abdel-Ouahab BOUDRAA , Abdelkhalek BOUCHIKHI , ... Monia TURKI-HADJ ALOUANE ∗ and El-Hadji Samba DIOP ... ∗ Unité Signaux et Syst`emes, ENIT BP 37, Le Belvedre 1002 Tunis, Tunisia. IRENav, Ecole Navale/ E 3 I 2(EA3876), ...

Journal of Communications and Networks, 2007

The conventional normalized least mean square (NLMS) algorithm is the most widely used for adapti... more The conventional normalized least mean square (NLMS) algorithm is the most widely used for adaptive identification within a non-stationary input context. The convergence of the NLMS algorithm is independent of environmental changes. However, its steady state performance is impaired during input sequences with low dynamics. In this paper, we propose a new NLMS algorithm which is, in the steady state, insensitive to the time variations of the input dynamics. The square soot (SR)-NLMS algorithm is based on a normalization of the LMS adaptive filter input by the Euclidean norm of the tap-input. The tap-input power of the SR-NLMS adaptive filter is then equal to one even during sequences with low dynamics. Therefore, the amplification of the observation noise power by the tap-input power is cancelled in the misadjustment time evolution. The harmful effect of the low dynamics input sequences, on the steady state performance of the LMS adaptive filter are then reduced. In addition, the square root normalized input is more stationary than the base input. Therefore, the robustness of LMS adaptive filter with respect to the input non stationarity is enhanced. A performance analysis of the first-and the second-order statistic behavior of the proposed SR-NLMS adaptive filter is carried out. In particular, an analytical expression of the step size ensuring stability and mean convergence is derived. In addition, the results of an experimental study demonstrating the good performance of the SR-NLMS algorithm are given. A comparison of these results with those obtained from a standard NLMS algorithm, is performed. It is shown that, within a nonstationary input context, the SR-NLMS algorithm exhibits better performance than the NLMS algorithm.