Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 77 days
Avg accept to publ: 48 days
APC: 300 EUR


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


TRAFFIC STATS

2,532,623 unique visits
1,006,935 downloads
Since November 1, 2009



Robots online now
PetalBot
bingbot


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 24 (2024)
 
     »   Issue 1 / 2024
 
 
 Volume 23 (2023)
 
     »   Issue 4 / 2023
 
     »   Issue 3 / 2023
 
     »   Issue 2 / 2023
 
     »   Issue 1 / 2023
 
 
 Volume 22 (2022)
 
     »   Issue 4 / 2022
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
  View all issues  


FEATURED ARTICLE

Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
Issue 2/2022

AbstractPlus






LATEST NEWS

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.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

Read More »


    
 

  2/2022 - 9

A Novel Approach to Speech Enhancement Based on Deep Neural Networks

SALEHI, M. See more information about SALEHI, M. on SCOPUS See more information about SALEHI, M. on IEEExplore See more information about SALEHI, M. on Web of Science, MIRZAKUCHAKI, S. See more information about MIRZAKUCHAKI, S. on SCOPUS See more information about MIRZAKUCHAKI, S. on SCOPUS See more information about MIRZAKUCHAKI, S. on Web of Science
 
View the paper record and citations in View the paper record and citations in Google Scholar
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 (3,059 KB) | Citation | Downloads: 655 | Views: 1,120

Author keywords
long short-term memory, machine learning, mean square error methods, recurrent neural networks, speech enhancement

References keywords
speech(31), processing(15), access(14), enhancement(13), learning(11), signal(9), noise(9), estimation(8), deep(8), spectral(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-05-31
Volume 22, Issue 2, Year 2022, On page(s): 71 - 78
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.02009
Web of Science Accession Number: 000810486800009
SCOPUS ID: 85131727505

Abstract
Quick view
Full text preview
Minimum mean-square error (MMSE) approaches have been shown to achieve state-of-the-art performance on the task of speech enhancement. However, MMSE approaches lack the ability to accurately estimate non-stationary noise sources. In this paper, a long short-term memory fully convolutional network (LSTM-FCN) is utilized to accurately estimate a priori signal-to-noise ratio (SNR) since the speech enhancement performance of an MMSE approach improves with the accuracy of the used a priori SNR estimator. The proposed MMSE approach makes no assumptions about the characteristics of the noise or the speech. MMSE approaches that utilize the LSTM-FCN estimator are evaluated using the mean opinion score of the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility (STOI) measures of speech. The experimental investigation shows that the speech enhancement performance of an MMSE approach that utilizes LSTM-FCN estimator significantly increases.


References | Cited By  «-- Click to see who has cited this paper

[1] S. K. Roy, A. Nicolson, K. K. Paliwal, "DeepLPC: A deep learning approach to augmented Kalman filter-based single-channel speech enhancement," IEEE Access, vol. 9, no. 4, pp. 64524-64538, 2021.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 13]


[2] Z. Q. Wang, P. Wang, D. Wang, "Complex spectral mapping for single-and multi-channel speech enhancement and robust ASR," IEEE/ACM Trans. Audio, Speech, and Language Processing, vol. 28, no. 5, pp. 1778 -1787, 2020.
[CrossRef] [Web of Science Times Cited 98] [SCOPUS Times Cited 129]


[3] S. Othman, A. Mohamed, A. Abouali, Z. Nossair, "Lossy compression using adaptive polynomial image encoding," Advances in Electrical and Computer Engineering, vol.21, no.1, pp.91-98, 2021.
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 5]


[4] T. G. Yadava, H. S. Jayanna, "Speech enhancement by combining spectral subtraction and minimum mean square error-spectrum power estimator based on zero crossing," International Journal of Speech Technology, vol. 22, no. 3, pp. 639-648, 2019.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 17]


[5] Y. Zhang, Y. Zhao, "Real and imaginary modulation spectral subtraction for speech enhancement," Speech Communication, vol. 55, no. 4, pp. 509-522, 2013.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 42]


[6] Y. Ephraim, D. Malah, "Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator," IEEE Trans. on Acoustics, Speech, and Signal processing, vol. 32, no. 6, pp. 1109-1121, 1984.
[CrossRef] [Web of Science Times Cited 2516] [SCOPUS Times Cited 3128]


[7] P. C. Loizou, Speech enhancement: Theory and practice. CRC press, 2007

[8] B. M. Mahmmod, A. R. Ramli, S. H. Abdulhussian, S. A. R. Al-Haddad, W. A. Jass, "Low-distortion MMSE speech enhancement estimator based on Laplacian Prior," IEEE Access, vol. 5, no. 4, pp. 9866-9881, 2017.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 37]


[9] A. Brown, S. Garg, J. Montgomery, "Automatic and efficient denoising of bioacoustics recordings using MMSE STSA," IEEE Access, vol. 6, no. 12, pp. 5010-5022, 2017.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 21]


[10] Q. Zhang, M. Wang, Y. Lu, M. Idrees, L. Zhang, "Fast nonstationary noise tracking based on log-spectral power MMSE estimator and temporal recursive averaging," IEEE Access, vol. 7, no. 6, pp. 80985-80999, 2019.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 10]


[11] C. Plapous, C. Marro, L. Mauuary, P. Scalart, "A two-step noise reduction technique," in IEEE International Conf. on Acoustics, Speech, and Signal Processing, Montreal, 2004, pp. 289-292.
[CrossRef]


[12] C. Plapous, C. Marro, P. Scalart, "Improved signal-to-noise ratio estimation for speech enhancement," IEEE Trans. on Audio, Speech, and Language Processing, vol. 14, no. 6, pp. 2098-2108, 2006.
[CrossRef] [Web of Science Times Cited 211] [SCOPUS Times Cited 277]


[13] Y. G. Thimmaraja, B. Nagaraja, H. Jayanna, "Speech enhancement and encoding by combining SS-VAD and LPC," International Journal of Speech Technology, vol. 24, no. 1, pp. 165-172, 2021.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 16]


[14] F. Bellili, R. Meftehi, S. Affes, A. Stephenne, "Maximum likelihood SNR estimation of linearly-modulated signals over time-varying flat-fading SIMO channels," IEEE Trans. on Signal Processing, vol. 63, no. 2, pp. 441-456, 2014.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 33]


[15] C. Breithaupt, T. Gerkmann, R. Martin, "A novel a priori SNR estimation approach based on selective cepstro-temporal smoothing," in IEEE International Conf. on Acoustics, Speech and Signal Processing, Las Vegas, 2008, pp. 4897-4900.
[CrossRef] [Web of Science Times Cited 92] [SCOPUS Times Cited 121]


[16] V. Timcenko, S. Gajin, "Machine learning enhanced entropy-based network anomaly detection," Advances in Electrical and Computer Engineering, vol.21, no.4, pp.51-60, 2021.
[CrossRef] [Full Text] [SCOPUS Times Cited 5]


[17] A. Albu, R. E Precup, T. A Teban, "Results and challenges of artificial neural networks used for decision-making in medical applications," FACTA Universitatis Series: Mechanical Engineering, vol. 17, no. 3, pp. 285-308, 2019.
[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 107]


[18] T. Zhang, F. Xu, T. Wu, "A software tool for spiking neural P systems," Romanian Journal of Information Science and Technology, vol. 23, no. 1, pp. 84-92, 2020

[19] E. L. Hedrea, R. E. Precup, R. C. Roman, E. M. Petriu, "Tensor product-based model transformation approach to tower crane systems modeling," Asian Journal of Control, vol. 23, no. 3, pp. 1313-1323, 2021.
[CrossRef] [Web of Science Times Cited 69] [SCOPUS Times Cited 72]


[20] J. B. Awotunde, R. O. Ogundokun, F. E. Ayo, O. E. Matiluko, "Speech segregation in background noise based on deep learning," IEEE Access, vol. 8, no. 9, pp. 169568-169575, 2020.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 11]


[21] J. Kim, M. Hahn, "Speech enhancement using a two-stage network for an efficient boosting strategy," IEEE Signal Processing Letters, vol. 26, no. 5, pp. 770-774, 2019.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 15]


[22] X. L. Zhang, D. Wang, "Boosting contextual information for deep neural network based voice activity detection," IEEE/ACM Trans. on Audio, Speech, and Language Processing, vol. 24, no. 2, pp. 252-264, 2015.
[CrossRef] [Web of Science Times Cited 104] [SCOPUS Times Cited 120]


[23] Q. Wang, J. Du, L. R. Dai, C. H. Lee, "A multiobjective learning and ensembling approach to high-performance speech enhancement with compact neural network architectures," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 7, pp. 1185-1197, 2018.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 40]


[24] I. Ahmed, S. Alam, J. Hossain, G. Kaddoum, "Deep learning for MMSE estimation of a Gaussian source in the presence of Bursty impulsive noise," IEEE Communications Letters, vol. 25, no. 4, pp. 1211-1215, 2020.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]


[25] A. Nicolson, K. K. Paliwal, "Deep learning for minimum mean-square error approaches to speech enhancement," Speech Communication, vol. 111, no. 8, pp. 44-55, 2019.
[CrossRef] [Web of Science Times Cited 78] [SCOPUS Times Cited 103]


[26] Q. Zhang, A. Nicolson, M. Wang, K. K. Paliwal, C. Wang, "DeepMMSE: A deep learning approach to MMSE-based noise power spectral density estimation," IEEE/ACM Trans. on Audio, Speech, and Language Processing, vol. 28, no. 4, pp. 1404-1415, 2020.
[CrossRef] [Web of Science Times Cited 70] [SCOPUS Times Cited 95]


[27] F. Karim, S. Majumdar, H. Darabi, S. Chen, "LSTM fully convolutional networks for time series classification," IEEE Access, vol. 6, no. 12, pp. 1662-1669, 2017.
[CrossRef] [Web of Science Times Cited 679] [SCOPUS Times Cited 881]


[28] F. Karim, S. Majumdar, H. Darabi, "Insights into LSTM fully convolutional networks for time series classification," IEEE Access, vol.7, no. 5, pp. 67718-67725, 2019.
[CrossRef] [Web of Science Times Cited 107] [SCOPUS Times Cited 131]


[29] R. A. Yu, "A low-complexity noise estimation algorithm based on smoothing of noise power estimation and estimation bias correction," in IEEE International Conf. on Acoustics, Speech and Signal Processing, Taipei, 2009, pp. 4421-4424.
[CrossRef] [SCOPUS Times Cited 39]


[30] R. C. Hendriks, R. Heusdens, J. Jensen, "MMSE based noise PSD tracking with low complexity," in IEEE International Conf. on Acoustics, Speech and Signal Processing, Dallas, 2010, pp. 4266-4269.
[CrossRef] [Web of Science Times Cited 174] [SCOPUS Times Cited 228]


[31] Y. Ephraim, D. Malah, "Speech enhancement using a minimum mean-square error log-spectral amplitude estimator," IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. 33, no. 2, pp. 443-445, 1985.
[CrossRef] [Web of Science Times Cited 1396] [SCOPUS Times Cited 1763]


[32] I. Goodfellow I, Y. Bengio, A. Courville, Deep learning. MIT press, pp. 257-267, 2016

[33] T. Y. Hsiao, Y. C. Chang, H. H. Chou, C. T. Lin, "Filter-based deep-compression with global average pooling for convolutional networks," Journal of Systems Architecture, vol. 95, no. 5, pp. 9-18, 2019.
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 71]


[34] C. LeaEmail, R. Vidal, A. Reiter, G. D. Hager, "Temporal Convolutional Networks: A unified approach to action segmentation," in European Conf. on Computer Vision, Amsterdam, 2016, pp. 47-54.
[CrossRef] [Web of Science Times Cited 392] [SCOPUS Times Cited 462]


[35] P. Dhruv, S. Naskar, "Image classification using convolutional neural network (CNN) and recurrent neural network (RNN): A review," Machine Learning and Information Processing, vol. 1101, no. 3, pp. 367-381, 2020.
[CrossRef] [SCOPUS Times Cited 48]


[36] X. Wu, X. Shen, J. Zhang, Y. Zhang, "A wind energy prediction scheme combining cauchy variation and reverse learning strategy," Advances in Electrical and Computer Engineering, vol.21, no.4, pp.3-10, 2021,
[CrossRef] [Full Text] [SCOPUS Times Cited 7]


[37] A. Barakat, P. Bianchi, "Convergence and dynamical behavior of the ADAM algorithm for non-convex stochastic optimization," SIAM Journal on Optimization, vol. 31, no. 1, pp. 244-274, 2021.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 34]


[38] P. Netrapalli, "Stochastic gradient descent and its variants in machine learning," Journal of the Indian Institute of Science, vol. 99, no. 2, pp. 201-213, 2019.
[CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 54]


[39] R. V. K. Reddy, B. S. Rao, K. P. Raju, "Handwritten Hindi digits recognition using convolutional neural network with RMSprop optimization," in Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, 2018, pp. 45-51.
[CrossRef] [SCOPUS Times Cited 45]


[40] C. H. Taal, R. C. Hendriks, R. Heusdens, J. Jensen, "An algorithm for intelligibility prediction of time-frequency weighted noisy speech," IEEE Trans. on Audio, Speech, and Language Processing, vol. 17, no. 7, pp. 2125-2136, 2011.
[CrossRef] [Web of Science Times Cited 1381] [SCOPUS Times Cited 1695]




References Weight

Web of Science® Citations for all references: 7,790 TCR
SCOPUS® Citations for all references: 9,880 TCR

Web of Science® Average Citations per reference: 190 ACR
SCOPUS® Average Citations per reference: 241 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-04-20 00:40 in 229 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: 


DNS Made Easy