Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zheng, Chengshi, Zhang, Huiyong, Liu, Wenzhe, Luo, Xiaoxue, Li, Andong, Li, Xiaodong, Moore, Brian C. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
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
_version_ 1785148220442148864
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
work_keys_str_mv AT zhengchengshi sixtyyearsoffrequencydomainmonauralspeechenhancementfromtraditionaltodeeplearningmethods
AT zhanghuiyong sixtyyearsoffrequencydomainmonauralspeechenhancementfromtraditionaltodeeplearningmethods
AT liuwenzhe sixtyyearsoffrequencydomainmonauralspeechenhancementfromtraditionaltodeeplearningmethods
AT luoxiaoxue sixtyyearsoffrequencydomainmonauralspeechenhancementfromtraditionaltodeeplearningmethods
AT liandong sixtyyearsoffrequencydomainmonauralspeechenhancementfromtraditionaltodeeplearningmethods
AT lixiaodong sixtyyearsoffrequencydomainmonauralspeechenhancementfromtraditionaltodeeplearningmethods
AT moorebriancj sixtyyearsoffrequencydomainmonauralspeechenhancementfromtraditionaltodeeplearningmethods