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JCR Impact Factor: 0.800
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SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  2/2018 - 13

 HIGHLY CITED PAPER 

Combination of Long-Term and Short-Term Features for Age Identification from Voice

BUYUK, O. See more information about BUYUK, O. on SCOPUS See more information about BUYUK, O. on IEEExplore See more information about BUYUK, O. on Web of Science, ARSLAN, M. L. See more information about ARSLAN, M. L. on SCOPUS See more information about ARSLAN, M. L. on SCOPUS See more information about ARSLAN, M. L. on Web of Science
 
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Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,172 KB) | Citation | Downloads: 892 | Views: 5,305

Author keywords
feature extraction, Gaussian mixture model, neural networks, speech processing, support vector machines

References keywords
processing(20), speaker(19), speech(16), recognition(14), signal(13), language(12), deep(9), verification(8), neural(8), vector(7)
No common words between the references section and the paper title.

About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 101 - 108
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02013
Web of Science Accession Number: 000434245000013
SCOPUS ID: 85047853422

Abstract
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In this paper, we propose to use Gaussian mixture model (GMM) supervectors in a feed-forward deep neural network (DNN) for age identification from voice. The GMM is trained with short-term mel-frequency cepstral coefficients (MFCC). The proposed GMM/DNN method is compared with a feed-forward DNN and a recurrent neural network (RNN) in which the MFCC features are directly used. We also make a comparison with the classical GMM and GMM/support vector machine (SVM) methods. Baseline results are obtained with a set of long-term features which are commonly used for age identification in previous studies. A feed-forward DNN and an SVM are trained using the long term features. All the systems are tested using a speech database which consists of 228 female and 156 male speakers. We define three age classes for each gender; young, adult and senior. In the experiments, the proposed GMM/DNN significantly outperforms all the other DNN types. Its performance is only comparable to the GMM/SVM method. On the other hand, experimental results show that age identification performance is significantly improved when the decisions of the short-term and long-term systems are combined together. We obtain approximately 4% absolute improvement with the combination compared to the best standalone system.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 4 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 10
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Cited-By CrossRef

[1] Speaker age and gender recognition using 1D and 2D convolutional neural networks, Yücesoy, Ergün, Neural Computing and Applications, ISSN 0941-0643, Issue 6, Volume 36, 2024.
Digital Object Identifier: 10.1007/s00521-023-09153-0
[CrossRef]

[2] Image Retrieval using One-Dimensional Color Histogram Created with Entropy, KILICASLAN, M., TANYERI, U., DEMIRCI, R., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 20, 2020.
Digital Object Identifier: 10.4316/AECE.2020.02010
[CrossRef] [Full text]

[3] Age and Gender Estimation Through Speech: A Comparison of Various Techniques, Shabbir, Maliha, Hussain, Amjad, Khan, Maqsood Muhammad, 2023 18th International Conference on Emerging Technologies (ICET), ISBN 979-8-3503-2817-2, 2023.
Digital Object Identifier: 10.1109/ICET59753.2023.10374670
[CrossRef]

[4] Technology as Infrastructure for Dehumanization:, Oviatt, Sharon, Proceedings of the 2021 International Conference on Multimodal Interaction, ISBN 9781450384810, 2021.
Digital Object Identifier: 10.1145/3462244.3482855
[CrossRef]

Updated 2 days, 13 hours ago

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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