Performance analysis of an improved split functional link adaptive filtering algorithm for nonlinear AEC.
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| Title: | Performance analysis of an improved split functional link adaptive filtering algorithm for nonlinear AEC. |
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| Authors: | Burra, Srikanth1 (AUTHOR) edm19d001@iiitdm.ac.in, Kar, Asutosh1 (AUTHOR) asutoshkar@iiitdm.ac.in |
| Source: | Applied Acoustics. May2021, Vol. 176, pN.PAG-N.PAG. 1p. |
| Subjects: | Adaptive filters, Mean square algorithms, Echo, White noise, Algorithms, Kalman filtering, Signal-to-noise ratio |
| Abstract: | In the process of nonlinear acoustic echo cancellation (NAEC), the eradication of echo becomes challenging due to the nonlinear distortion introduced by the low-cost hands-free communication devices. To mitigate the effect of these artifacts, various NAEC adaptive filtering algorithms are existing which find their applicability in different echoed environments. But, there exists a scope to improve the echo return loss enhancement (ERLE) performance and the rate of convergence for adaptive NAEC algorithms. These improvements can be achieved with further optimization of key parameters in existing mean square error based adaptive algorithms and by proposing a novel combination of linear and nonlinear adaptive filters. In this paper, a split functional link-based adaptive filter (SFLAF) is proposed with an improved optimized -normalized least mean square adaptive algorithm for NAEC. In addition to that, the convergence and steady-state analyses of the proposed algorithm were presented in this paper. The performance of the proposed algorithm is compared to existing SFLAF based NAEC algorithms, and improvements are presented. The colored noise signal, clean speech, and speech signal corrupted with white noise are subjected as inputs to the proposed NAEC framework under different signal-to-noise ratios. The ERLE, spectrograms, and perceptual evaluation of sound quality are used as the performance indices for the comparison of the proposed algorithm with its counterparts. An average improvement of 3 dB is observed in the case of the proposed algorithm compared to its counterparts. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Acoustics is the property of Elsevier B.V. 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: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 148776076 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Performance analysis of an improved split functional link adaptive filtering algorithm for nonlinear AEC. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Burra%2C+Srikanth%22">Burra, Srikanth</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> edm19d001@iiitdm.ac.in</i><br /><searchLink fieldCode="AR" term="%22Kar%2C+Asutosh%22">Kar, Asutosh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> asutoshkar@iiitdm.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Acoustics%22">Applied Acoustics</searchLink>. May2021, Vol. 176, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Adaptive+filters%22">Adaptive filters</searchLink><br /><searchLink fieldCode="DE" term="%22Mean+square+algorithms%22">Mean square algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Echo%22">Echo</searchLink><br /><searchLink fieldCode="DE" term="%22White+noise%22">White noise</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the process of nonlinear acoustic echo cancellation (NAEC), the eradication of echo becomes challenging due to the nonlinear distortion introduced by the low-cost hands-free communication devices. To mitigate the effect of these artifacts, various NAEC adaptive filtering algorithms are existing which find their applicability in different echoed environments. But, there exists a scope to improve the echo return loss enhancement (ERLE) performance and the rate of convergence for adaptive NAEC algorithms. These improvements can be achieved with further optimization of key parameters in existing mean square error based adaptive algorithms and by proposing a novel combination of linear and nonlinear adaptive filters. In this paper, a split functional link-based adaptive filter (SFLAF) is proposed with an improved optimized -normalized least mean square adaptive algorithm for NAEC. In addition to that, the convergence and steady-state analyses of the proposed algorithm were presented in this paper. The performance of the proposed algorithm is compared to existing SFLAF based NAEC algorithms, and improvements are presented. The colored noise signal, clean speech, and speech signal corrupted with white noise are subjected as inputs to the proposed NAEC framework under different signal-to-noise ratios. The ERLE, spectrograms, and perceptual evaluation of sound quality are used as the performance indices for the comparison of the proposed algorithm with its counterparts. An average improvement of 3 dB is observed in the case of the proposed algorithm compared to its counterparts. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Acoustics is the property of Elsevier B.V. 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: 10.1016/j.apacoust.2020.107863 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Adaptive filters Type: general – SubjectFull: Mean square algorithms Type: general – SubjectFull: Echo Type: general – SubjectFull: White noise Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Kalman filtering Type: general – SubjectFull: Signal-to-noise ratio Type: general Titles: – TitleFull: Performance analysis of an improved split functional link adaptive filtering algorithm for nonlinear AEC. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Burra, Srikanth – PersonEntity: Name: NameFull: Kar, Asutosh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 0003682X Numbering: – Type: volume Value: 176 Titles: – TitleFull: Applied Acoustics Type: main |
| ResultId | 1 |
