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Dive into the research topics where Raja Ghozi is active.

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Featured researches published by Raja Ghozi.


international conference on acoustics, speech, and signal processing | 2015

Temporal entropy-based texturedness indicator for audio signals

Olfa Fraj; Raja Ghozi; Meriem Jaïdane-Saïdane

In this paper, we present a temporal entropy-based indicator that reflects the texturedness level of a given audio signal. Inspired from an image homogeneity evaluation via a multidirectional cumulative entropy computation, we similarly propose an audio signal homogeneity analysis through a direct and reverse progressive auditory information content tracking. A [0 - 5] texturedness indicator is then constructed using auditory-inspired parameters, and is inherently associated with short listening time. Using this indicator, a new audio signals classification is proposed where speech signals are assigned low texturedness scores, and academic noise signals are assigned the higher range of texturedness scores. Classically known audio textures are assigned, in this scale, various intermediate to high texturedness scores.


information sciences, signal processing and their applications | 2012

Arabic adaptation of Phonology and Memory test using entropy-based analysis of word complexity

Sofiene Bacha; Raja Ghozi; Meriem Jaidane; Neziha Gouider-Khouja

Short-term Phonological Memory evaluation is very important in tracking the learning development of children, and for that, speech-therapists use words with different linguistic complexity levels. The test “Memory and Phonology” (PM) adopted in the Department of Child and Adolescent Neurology in the National Institute of Neurology (Tunisia), relies on a heuristically-constructed database of Tunisian-Arabic words, in analogy with the standardized NEEL PM French test. Given that the choice of a words phonology is very specific to the language and a costly experimental calibration must be done to validate the classification according to words levels difficulty, this work offers a phonology-based complexity algorithm that validates not only the French data base of the NEELs test, but also that currently proposed and adopted by the Tunisian speech therapists. The proposed complexity analysis and classification are based on three entropy measures: time-based, spectral, and the variance of dynamic spectral entropy of the sound signal of a given word.


acm symposium on applied computing | 2018

AffectiveROAD system and database to assess driver's attention

Neska El Haouij; Jean-Michel Poggi; Sylvie Sevestre-Ghalila; Raja Ghozi; Meriem Jaidane

Thanks to the rise of new wearable and non-intrusive sensor technology, Internet of Things (IoT) contributes in human daily life improvement. In the context of smart vehicles, human affective monitoring should be based on a context-aware system in order to consider the interactions between the driver, the vehicle and the ambient environment. In this paper, we propose AffectiveROAD platform, that senses the human physiological changes, the ambient environment inside the vehicle, and the vehicle speed. Thanks to this platform, several drivers state indicators such as stress and arousal may be developed and validated. Two types of wireless physiological sensors are used to monitor the electrodermal activity, the heart rate, the skin temperature, the respiration, and the hand movement of the driver. Moreover, we developed a sensor network allowing to capture the ambient temperature, humidity, pressure, and luminosity. The purpose of this paper is to describe a real-world driving protocol allowing to collect data using IoT-based materials and to announce the publication of a database for drivers state monitoring research. A partial database concerning the physiological and the environmental information is available on request, for public use.


Multimedia Tools and Applications | 2017

Audio texturedness indicator based on a direct and reverse short listening time analysis

Olfa Fraj; Raja Ghozi; Meriem Jaïdane-Saïdane

In this paper, we present an objective evaluation of audio texturedness level motivated by a subjective study which emphasizes the relevance of a direct and reverse short listening time analysis. This study was based on 77 undergraduate engineering students, where the concept of audio texturedness relied on audio and image analogies. Audio texturedness evaluation of a large audio database using a discrete [1 - 5] texturedness scale was performed. As a result, an objective audio texturedness indicator is proposed based on the cumulative average signal informational content change in the direct and reverse directions. This bidirectional cumulative entropy tracking was done in analogy with a classical multidirectional homogeneity method for image texture discrimination. As expected, the proposed indicator has ranked the class of noise on the high end of the [1 - 5] scale, whereas highly motivational speech signals ranked low on the proposed scale due to the large variations in their average informational content in the direct and reverse directions. This audio texturedness indicator offers a continuum of audio classes, in contrast with the classical noise, speech, and music sound categorization. The relevance of the proposed objective indicator auditory parameters was explored for a maximum objective-subjective cross-correlation and illustrated in a preliminary audio stream segmentation application.


applied sciences on biomedical and communication technologies | 2011

Presbyacousis and stress evaluation in urban settings

Raja Ghozi; Olfa Fraj; N. Khalfa; Meriem Jaidane; Faten Hussein

For the elderly, altered audio/speech perception has serious impact on emotional, physical and social well-being. For instance, hearing loss is the third impairment of the elderly after arthritis and hypertension. As the progressive loss of high frequency perception changed the appreciation of distance and location of a sound source, the mobility-related security issues are very clear in presbyacoustic people who are involved in 40% of fatal injuries and 1500 accidents/day as pedestrian and drivers [1].


Statistical Methods and Applications | 2018

Random Forest-Based Approach for Physiological Functional Variable Selection: Towards Driver's Stress Level Classification

Neska El Haouij; Jean-Michel Poggi; Raja Ghozi; Sylvie Sevestre-Ghalila; Meriem Jaidane

This paper deals with physiological functional variables selection for driver’s stress level classification using random forests. Our analysis is performed on experimental data extracted from the drivedb open database available on PhysioNet website. The physiological measurements of interest are: electrodermal activity captured on the driver’s left hand and foot, electromyogram, respiration, and heart rate, collected from ten driving experiments carried out in three types of routes (rest area, city, and highway). The contributions of this work touch on the method as well as the application aspects. From a methodological viewpoint, the physiological signals are considered as functional variables, decomposed on a wavelet basis and then analyzed in search of most relevant variables. On the application side, the proposed approach provides a “blind” procedure for driver’s stress level classification, giving close performances to those resulting from the expert-based approach, when applied to the drivedb database. It also suggests new physiological features based on the wavelet levels corresponding to the functional variables wavelet decomposition. Finally, the proposed approach provides a ranking of physiological variables according to their importance in stress level classification. For the case under study, results suggest that the electromyogram and the heart rate signals are less relevant compared to the electrodermal and the respiration signals. Furthermore, the electrodermal activity measured on the driver’s foot was found more relevant than the one captured on the hand. Finally, the proposed approach also provided an order of relevance of the wavelet features.


2016 International Symposium on Signal, Image, Video and Communications (ISIVC) | 2016

Texturedness decision time for audio texturedness indicator

Olfa Fraj; Raja Ghozi; Meriem Jaïdane-Saïdane

In this paper we analyze the texturedness decision time Td needed to evaluate the texturedness level of a given audio signal. We correlate the subjective values of Td with the objective time parameter Tobs of a recently proposed audio texturedness indicator [1]. As Tobs varies, speech texturedness measures are highly variant but got low texturedness values, whereas classic audio textures and academic noise got more stable and high texturedness values. A survey conducted on 56 subjects has shown that the very textured audio signals were quickly recognized and scored in a very short decision time, whereas speech signals decision time was higher. This subjective analysis suggests a rising time as the minimum time duration needed to make a texturedness score decision which must be included in the objective indicator.


Audio Engineering Society Conference: 60th International Conference: DREAMS (Dereverberation and Reverberation of Audio, Music, and Speech) | 2016

Non-Monotonic Impact of Occupancy Level on Reverberation Indicators: Case of a Public Confined Eating Establishment

Yosra Mzah; Seddik Maarfi; Raja Ghozi; Meriem Jaidane


Ambiances in action / Ambiances en acte(s) - International Congress on Ambiances, Montreal 2012 | 2012

Detection of situations of danger faced by old pedestrian in urban space via the segmentation of sound scenes

Faten Hussein; Gérard Hegron; Jean-Pierre Péneau; Pascal Joanne; Olfa Fraj; Raja Ghozi; Meriem Jaidane


Journal of The Audio Engineering Society | 2015

Occupancy-Based Analysis and Interpretation of Soundscape Auditory Complexity: Case of a Campus Restaurant

Raja Ghozi; Olfa Fraj; Mohsen Bel-haj Salem; Meriem Jaidane

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Jean-Michel Poggi

Paris Descartes University

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