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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  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
 
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Download PDF pdficon (3,059 KB) | Citation | Downloads: 286 | Views: 181

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
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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.


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References Weight

Web of Science® Citations for all references: 6,140 TCR
SCOPUS® Citations for all references: 7,880 TCR

Web of Science® Average Citations per reference: 150 ACR
SCOPUS® Average Citations per reference: 192 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 2022-08-11 19:40 in 235 seconds.




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