Academic Journal
Fundamentals, present and future perspectives of speech enhancement.
| Title: | Fundamentals, present and future perspectives of speech enhancement. |
|---|---|
| Authors: | Das, Nabanita, Chakraborty, Sayan, Chaki, Jyotismita, Padhy, Neelamadhab, Dey, Nilanjan |
| Source: | International Journal of Speech Technology; Dec2021, Vol. 24 Issue 4, p883-901, 19p |
| Subject Terms: | Deep learning, Speech enhancement, Speech processing systems, Feature selection, Machine learning, Sequential pattern mining, Feature extraction, Hearing aids |
| Abstract: | Speech enhancement has substantial interest in the utilization of speaker identification, video-conference, speech transmission through communication channels, speech-based biometric system, mobile phones, hearing aids, microphones, voice conversion etc. Pattern mining methods have a vital step in the growth of speech enhancement schemes. To design a successful speech enhancement system consideration to the background noise processing is needed. A substantial number of methods from traditional techniques and machine learning have been utilized to process and remove the additive noise from a speech signal. With the advancement of machine learning and deep learning, classification of speech has become more significant. Methods of speech enhancement consist of different stages, such as feature extraction of the input speech signal, feature selection, feature selection followed by classification. Deep learning techniques are also an emerging field in the classification domain, which is discussed in this review. The intention of this paper is to provide a state-of-the-art summary and present approaches for using the widely used machine learning and deep learning methods to detect the challenges along with future research directions of speech enhancement systems. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Speech Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10772-020-09674-2 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: Fundamentals, present and future perspectives of speech enhancement. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Das%2C+Nabanita%22">Das, Nabanita</searchLink><br /><searchLink fieldCode="AR" term="%22Chakraborty%2C+Sayan%22">Chakraborty, Sayan</searchLink><br /><searchLink fieldCode="AR" term="%22Chaki%2C+Jyotismita%22">Chaki, Jyotismita</searchLink><br /><searchLink fieldCode="AR" term="%22Padhy%2C+Neelamadhab%22">Padhy, Neelamadhab</searchLink><br /><searchLink fieldCode="AR" term="%22Dey%2C+Nilanjan%22">Dey, Nilanjan</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Speech Technology; Dec2021, Vol. 24 Issue 4, p883-901, 19p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+enhancement%22">Speech enhancement</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+processing+systems%22">Speech processing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+pattern+mining%22">Sequential pattern mining</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Hearing+aids%22">Hearing aids</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Speech enhancement has substantial interest in the utilization of speaker identification, video-conference, speech transmission through communication channels, speech-based biometric system, mobile phones, hearing aids, microphones, voice conversion etc. Pattern mining methods have a vital step in the growth of speech enhancement schemes. To design a successful speech enhancement system consideration to the background noise processing is needed. A substantial number of methods from traditional techniques and machine learning have been utilized to process and remove the additive noise from a speech signal. With the advancement of machine learning and deep learning, classification of speech has become more significant. Methods of speech enhancement consist of different stages, such as feature extraction of the input speech signal, feature selection, feature selection followed by classification. Deep learning techniques are also an emerging field in the classification domain, which is discussed in this review. The intention of this paper is to provide a state-of-the-art summary and present approaches for using the widely used machine learning and deep learning methods to detect the challenges along with future research directions of speech enhancement systems. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Speech Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10772-020-09674-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 883 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Speech enhancement Type: general – SubjectFull: Speech processing systems Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Sequential pattern mining Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Hearing aids Type: general Titles: – TitleFull: Fundamentals, present and future perspectives of speech enhancement. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Das, Nabanita – PersonEntity: Name: NameFull: Chakraborty, Sayan – PersonEntity: Name: NameFull: Chaki, Jyotismita – PersonEntity: Name: NameFull: Padhy, Neelamadhab – PersonEntity: Name: NameFull: Dey, Nilanjan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 13812416 Numbering: – Type: volume Value: 24 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Speech Technology Type: main |
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