Mobile, Secure and Programmable Networking (MSPN'2019)

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Cloud Continuum and Enabled Applications".

Deadline for manuscript submissions: closed (20 October 2019) | Viewed by 5809

Special Issue Editors


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Guest Editor
LIGM, University Gustave Eiffel, CNRS, ESIEE Paris, 93162 Noisy-le-Grand, France
Interests: communication; security; machine learning; environmental issues
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Guest Editor
Faculté des Sciences et Techniques, Mohammedia, Morocco
Interests: wireless mesh networks; ad hoc networks; sensor networks; cellular networks; multi-user MIMO communications

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Guest Editor
Conservatoire National des Arts et Métiers, 75003 Paris, France
Interests: networks; wireless networks; VANETs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

MSPN 2019 is the 5th edition of the successful International Conference on Mobile, Secure and Programmable Networking. The goal of the conference is to provide a forum for scientists, engineers and researchers to discuss and exchange novel ideas, results, experiences and work-in-progress on all aspects of computer and information technology. Each year, MSPN attendees appreciate and benefit from multidisciplinary exchanges on Mobile, Secure and Programmable Networking. For more information about the conference, please use this link: http://www.adda-association.org/mspn-2019/ .

Selected authors who present papers at the conference are invited to submit their extended versions to this Special Issue of the journal Computers after the conference. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open-access format in Computers and collected together in this Special Issue website.

The conference paper should be cited and noted on the first page of the paper; authors are asked to disclose that it is a conference paper in their cover letter, and include a statement on what has been changed compared to the original conference paper; submitted papers should be extended to the size of regular research or review articles with at least 40% extension of new results, and the papers should be not exceed 30% copy/paste from conference paper.

Please prepare and format your paper according to the Instructions for Authors. Use the LaTeX or Microsoft Word template file for the journal (both are available from the Instructions for Authors page). Manuscripts should be submitted online via our susy.mdpi.com editorial system.

Dr. Eric Renault
Dr. Cherkaoui Leghris
Dr. Selma Boumerdassi
Guest Editors

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Keywords

  • SDN
  • virtualization
  • mobile, cloud and fog computing
  • NFV
  • 5G networks
  • intent-based networking
  • IoT
  • VANETs
  • cognitive radio
  • location-based Services

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Published Papers (1 paper)

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Research

15 pages, 281 KiB  
Article
An Investigation of a Feature-Level Fusion for Noisy Speech Emotion Recognition
by Sara Sekkate, Mohammed Khalil, Abdellah Adib and Sofia Ben Jebara
Computers 2019, 8(4), 91; https://doi.org/10.3390/computers8040091 - 13 Dec 2019
Cited by 18 | Viewed by 5283
Abstract
Because one of the key issues in improving the performance of Speech Emotion Recognition (SER) systems is the choice of an effective feature representation, most of the research has focused on developing a feature level fusion using a large set of features. In [...] Read more.
Because one of the key issues in improving the performance of Speech Emotion Recognition (SER) systems is the choice of an effective feature representation, most of the research has focused on developing a feature level fusion using a large set of features. In our study, we propose a relatively low-dimensional feature set that combines three features: baseline Mel Frequency Cepstral Coefficients (MFCCs), MFCCs derived from Discrete Wavelet Transform (DWT) sub-band coefficients that are denoted as DMFCC, and pitch based features. Moreover, the performance of the proposed feature extraction method is evaluated in clean conditions and in the presence of several real-world noises. Furthermore, conventional Machine Learning (ML) and Deep Learning (DL) classifiers are employed for comparison. The proposal is tested using speech utterances of both of the Berlin German Emotional Database (EMO-DB) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) speech databases through speaker independent experiments. Experimental results show improvement in speech emotion detection over baselines. Full article
(This article belongs to the Special Issue Mobile, Secure and Programmable Networking (MSPN'2019))
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