Teager energy based feature parameters for speech recognition in car noise

Cataloged from PDF version of article.In this letter, a new set of speech feature parameters based on multirate signal processing and the Teager energy operator is introduced. The speech signal is first divided into nonuniform subbands in mel-scale using a multirate filterbank, then the Teager energies of the subsignals are estimated. Finally, the feature vector is constructed by log-compression and inverse discrete cosine transform (DCT) computation. The new feature parameters have robust speech recognition performance in the presence of car engine noise

MAP REQUIREMENT ENGINEER MODEL (SHIPPING DOMAIN)

The failures in the implementation and use of computer systems are due in numerous studies to a bad comprehension of the needs that these systems attempt to answer. The consequences of this misunderstanding can be critical and needs huge efforts to deal with the situation. So, it is necessary to define methods, technologies and tools to clarify, validate and represent needs relative to the systems on appropriate and structured manner. In this article, we will summarize main characteristics of different requirement modeling approaches such us I star, EKD, KAOS…therefore we choose MAP as the most adequate modeling approach. And we propose a model based on MAP in the Shipping domain. Indeed, our choice of the model Map approach has been based on a comparison of different characteristics and contributions of t e MAP compared to other models. In addition to that we choose the field of shipping, because of the abundance of stakeholders and activities (business process), to present needs in map model. A concrete model of the shipping import service allows us to on the alignment between organizational and operational sight of a company. This rapprochement is fundamental to ensure dynamism imposed to all system to be update facing a rapid and continuous evolution

Large vocabulary speech recognition in noisy environments

Ankara : Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1998.Thesis (Master's) -- Bilkent University, 1998.Includes bibliographical references leaves 48-52A ІКПѴ set of speech feature parameters based on multirate subband analysis and the Teager Energy Operator (TEO) is developed. The speech signal is first divided into nonuniform subbands in mel-scale using a multirate filter-bank, then the Teager energies of the subsignals are estimated. Finally, the feature vector is constructed by logcompression and inverse DOT computation. The new feature parameters (TEOCEP) have a robust speech recognition performance in car engine noise which has a low pass nature. In this thesis, we also present some solutions to the problem of large vocabulary speech recognition. Triphone-based Hidden Markov. Models (HMM) are used to model the vocabulary words. Although the straight forward parallel search strategy gives good recognition performance, the processing time required is found to be long and impractical. Therefore another search strategy with similar performance is described. Subvocabularies are developed during the training session to reduce the total number of words considered in the search process. The search is then performed in a tree structure by investigating one subvocabulary instead of all the words.Jabloun, FirasM.S

ROLE ON REQUIREMENTS ENGINEERING PROCESS MODEL

The contribution of the requirements engineering in the success of projects is incontestable and it has been proved, through studies and statistics that succeed or fail a project depends generally on requirements engineering. This domain became the centre of interest of a big number of researchers to present the most adequate model and process to succeed the requirements engineering and so succeed the project. Requirements engineering is done according to a process spread all along the life cycle of the system and that aims to provide methods, technologies and tools allow developing and implanting some computing systems providing the services and the information expected by their users, demanded by their acquirers, and compatible with their functioning environment. Requirements engineering process is executed on three principals steps: First, the requirements elicitation to identify future system needs .Then, the specification and the requirements documentation validated or modified following a discussion between the different actors of the process. The end product which is an outfit of models that allows to take into account the actors’ point of view, represent not only the final specifications but also the intermediate results. This permits having coherent and well structured documents. The validation and verification of the requirement specification is the final step. On this article, after a presentation of principal challenges that requirements engineering face and notably in specification step, we will define the fundamental notion of a role that will allow us to surround a system in all his perspectives : the organizational, the functional and even the intentional, etc. Then, after a study of some models of requirements engineering we will propose a model of process of requirements engineering with focusing on role notation and its participation in the different process steps

Teager energy based feature parameters for robust speech recognition in car noise

In this paper, a new set of speech feature parameters based on multirate signal processing and the Teager Energy Operator is developed. The speech signal is first divided into nonuniform subbands in mel-scale using a multirate filter-bank, then the Teager energies of the subsignals are estimated. Finally, the feature vector is constructed by log-compression and inverse DCT computation. The new feature parameters have a robust speech recognition performance in car engine noise which is low pass in nature

A Reversible Jump MCMC Algorithm for Particle Size Inversion in Multiangle Dynamic Light Scattering

Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 201

Estimation et reconstruction des signaux courts multicomposantes modulées non-linéairement à la fois en amplitude et en fréquence

- Dans ce papier, nous considérons des signaux, courts, non-stationnaires, modulés non linéairement en amplitude et en fréquence. Nous étendons une approche locale, développée dans le cadre des signaux monocomposante et dont l'efficacité et la robustesse ont été prouvées [3, 4]. Nous utilisons un modèle polynomial pour la fréquence et l'amplitude instantanées (FI / AI). Les paramètres du modèle sont ensuite estimés en maximisant la Vraisemblance par une technique d'optimisation stochastique : le Recuit Simulé. Basés sur la même démarche et dans le contexte des signaux multicomposantes, nous comparons deux approches différentes. Une première approche, optimale, et qui s'avère coûteuse en temps de calcul, consiste à estimer tous les paramètres du modèle à la fois. La deuxième approche, sous-optimale, reconstruit itérativement le signal composante par composante. Des simulations de Monte Carlo et une comparaison avec les Bornes de Cramer Rao illustrant les bonnes performances seront présentées. Nous obtenons une bonne estimation dans le cas où les fréquences instantanées se croisent, ce qui constitue une bonne performance compte tenu du faible nombre d'échantillons. Les deux approches sont testées ensuite sur des données réelles

Future socioeconomic conditions may have a larger impact than climate change on nutrient loads to the Baltic Sea

The Baltic Sea is suffering from eutrophication caused by nutrient discharges from land to sea, and these loads might change in a changing climate. We show that the impact from climate change by mid-century is probably less than the direct impact of changing socioeconomic factors such as land use, agricultural practices, atmospheric deposition, and wastewater emissions. We compare results from dynamic modelling of nutrient loads to the Baltic Sea under projections of climate change and scenarios for shared socioeconomic pathways. Average nutrient loads are projected to increase by 8% and 14% for nitrogen and phosphorus, respectively, in response to climate change scenarios. In contrast, changes in the socioeconomic drivers can lead to a decrease of 13% and 6% or an increase of 11% and 9% in nitrogen and phosphorus loads, respectively, depending on the pathway. This indicates that policy decisions still play a major role in climate adaptation and in managing eutrophication in the Baltic Sea region.Peer reviewe

Sensitivity of simulated crop yield and nitrate leaching of the wheat-maize cropping system in the North China Plain to model parameters

Process-based crop simulation models are often over-parameterised and are therefore difficult to calibrate properly. Following this rationale, the Morris screening sensitivity method was carried out on the DAISY model to identify the most influential input parameters operating on selected model outputs, i.e. crop yield, grain nitrogen (N), evapotranspiration and N leaching. The results obtained refer to the winter wheat-summer maize cropping system in the North China Plain. In this study, four different N fertiliser treatments over six years were considered based on a randomised field experiment at Luancheng Experimental Station to elucidate the impact of weather and nitrogen inputs on model sensitivity. A total of 128 parameters were considered for the sensitivity analysis. The ratios [output changes/parameter increments] demonstrated high standard deviations for the most relevant parameters, indicating high parameter non-linearity/interactions. In general, about 34 parameters influenced the outputs of the DAISY model for both crops. The most influential parameters depended on the output considered with sensitivity patterns consistent with the expected dominant processes. Interestingly, some parameters related to the previous crop were found to affect output variables of the following crop, illustrating the importance of considering crop sequences for model calibration. The developed RDAISY toolbox used in this study can serve as a basis for following sensitivity analysis of the DAISY model, thus enabling the selection of the most influential parameters to be considered with model calibration

Speech Features for Discriminating Stress Using Branch and Bound Wrapper Search

Stress detection from speech is a less explored field than Automatic Emotion Recognition and it is still not clear which features are better stress discriminants. VOCE aims at doing speech classification as stressed or not-stressed in real-time, using acoustic-prosodic features only. We therefore look for the best discriminating feature subsets from a set of 6285 features – 6125 features extracted with openSMILE toolkit and 160 Teager Energy Operator (TEO) features. We use a mutual information filter and a branch and bound wrapper heuristic with an SVM classifier to perform feature selection. Since many feature sets are selected, we analyse them in terms of chosen features and classifier performance concerning also true positive and false positive rates. The results show that the best feature types for our application case are Audio Spectral, MFCC, PCM and TEO. We reached results as high as 70.36% for generalisation accuracyinfo:eu-repo/semantics/publishedVersio