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Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods
Frequency-domain monaural speech enhancement has been extensively studied for over 60 years, and a great number of methods have been proposed and applied to many devices. In the last decade, monaural speech enhancement has made tremendous progress with the advent and development of deep learning, an...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10658184/ https://www.ncbi.nlm.nih.gov/pubmed/37956661 http://dx.doi.org/10.1177/23312165231209913 |
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author | Zheng, Chengshi Zhang, Huiyong Liu, Wenzhe Luo, Xiaoxue Li, Andong Li, Xiaodong Moore, Brian C. J. |
author_facet | Zheng, Chengshi Zhang, Huiyong Liu, Wenzhe Luo, Xiaoxue Li, Andong Li, Xiaodong Moore, Brian C. J. |
author_sort | Zheng, Chengshi |
collection | PubMed |
description | Frequency-domain monaural speech enhancement has been extensively studied for over 60 years, and a great number of methods have been proposed and applied to many devices. In the last decade, monaural speech enhancement has made tremendous progress with the advent and development of deep learning, and performance using such methods has been greatly improved relative to traditional methods. This survey paper first provides a comprehensive overview of traditional and deep-learning methods for monaural speech enhancement in the frequency domain. The fundamental assumptions of each approach are then summarized and analyzed to clarify their limitations and advantages. A comprehensive evaluation of some typical methods was conducted using the WSJ + Deep Noise Suppression (DNS) challenge and Voice Bank + DEMAND datasets to give an intuitive and unified comparison. The benefits of monaural speech enhancement methods using objective metrics relevant for normal-hearing and hearing-impaired listeners were evaluated. The objective test results showed that compression of the input features was important for simulated normal-hearing listeners but not for simulated hearing-impaired listeners. Potential future research and development topics in monaural speech enhancement are suggested. |
format | Online Article Text |
id | pubmed-10658184 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-106581842023-11-13 Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods Zheng, Chengshi Zhang, Huiyong Liu, Wenzhe Luo, Xiaoxue Li, Andong Li, Xiaodong Moore, Brian C. J. Trends Hear Original Article Frequency-domain monaural speech enhancement has been extensively studied for over 60 years, and a great number of methods have been proposed and applied to many devices. In the last decade, monaural speech enhancement has made tremendous progress with the advent and development of deep learning, and performance using such methods has been greatly improved relative to traditional methods. This survey paper first provides a comprehensive overview of traditional and deep-learning methods for monaural speech enhancement in the frequency domain. The fundamental assumptions of each approach are then summarized and analyzed to clarify their limitations and advantages. A comprehensive evaluation of some typical methods was conducted using the WSJ + Deep Noise Suppression (DNS) challenge and Voice Bank + DEMAND datasets to give an intuitive and unified comparison. The benefits of monaural speech enhancement methods using objective metrics relevant for normal-hearing and hearing-impaired listeners were evaluated. The objective test results showed that compression of the input features was important for simulated normal-hearing listeners but not for simulated hearing-impaired listeners. Potential future research and development topics in monaural speech enhancement are suggested. SAGE Publications 2023-11-13 /pmc/articles/PMC10658184/ /pubmed/37956661 http://dx.doi.org/10.1177/23312165231209913 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Article Zheng, Chengshi Zhang, Huiyong Liu, Wenzhe Luo, Xiaoxue Li, Andong Li, Xiaodong Moore, Brian C. J. Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods |
title | Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods |
title_full | Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods |
title_fullStr | Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods |
title_full_unstemmed | Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods |
title_short | Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods |
title_sort | sixty years of frequency-domain monaural speech enhancement: from traditional to deep learning methods |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10658184/ https://www.ncbi.nlm.nih.gov/pubmed/37956661 http://dx.doi.org/10.1177/23312165231209913 |
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