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A Novel Approach to Speech Enhancement Based on Deep Neural NetworksSALEHI, M. , MIRZAKUCHAKI, S. |
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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
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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[1] Steel Surface Defect Detection Method Based on Improved YOLOv9 Network, Zou, Jialin, Wang, Hongcheng, IEEE Access, ISSN 2169-3536, Issue , 2024.
Digital Object Identifier: 10.1109/ACCESS.2024.3453931 [CrossRef]
[2] Deep learning for speech denoising with improved Wiener approach, Abdelli, Ouardia, Merazka, Fatiha, International Journal of Speech Technology, ISSN 1381-2416, 2024.
Digital Object Identifier: 10.1007/s10772-024-10148-y [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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