Nonlinear acoustic echo cancellation using low-complexity low-rank recursive least-squares algorithms
Adaptive exponential functional link networks (AEFLN) are a type of linear-in-the-parameter nonlinear filters, which have shown enhanced modeling capability for nonlinear systems. However, the convergence speed of traditional AEFLN is generally slow for long impulse responses, hence, AEFLN trained u...
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| Veröffentlicht in: | Signal processing Jg. 225; S. 109623 |
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| Sprache: | Englisch |
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01.12.2024
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| ISSN: | 0165-1684 |
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| Abstract | Adaptive exponential functional link networks (AEFLN) are a type of linear-in-the-parameter nonlinear filters, which have shown enhanced modeling capability for nonlinear systems. However, the convergence speed of traditional AEFLN is generally slow for long impulse responses, hence, AEFLN trained using recursive least-squares becomes an attractive choice to achieve faster convergence. Moreover, huge computational burden of RLS makes it unsuitable in practical applications like echo cancellation. While low complexity versions of RLS are widely available in literature, they still suffer from low convergence speed. To address this issue, we propose a nonlinear acoustic echo cancellation (NAEC) system using AEFLN-RLS, based on the nearest Kronecker product (NKP) decomposition and low-rank approximation technique, which not only reduces computational complexity but also achieves improved convergence speed (especially tracking). To further improve the echo cancellation performance in non-stationary conditions, a variable regularization approach based NKP-AEFLN-RLS system is also proposed. To also reduce the computational complexity further, dichotomous coordinate descent (DCD) updates are incorporated into the proposed NKP-AEFLN-RLS NAEC system and its variable regularization version. Experimental results show the effectiveness of the proposed algorithms, with the variable regularized version of the algorithm using DCD iterations showing the best compromise between convergence capability and lower computational complexity.
•A NKPD based adaptive exponential functional link structure updated using the RLS is developed.•Near optimal VR scheme VR-NKP-AEFLN-RLS which updates the regularization parameter at each step.•Reduced computation complexity variant of proposed algorithms using a DCD approach is developed.•A detailed comparison of the computational complexity of proposed algorithms is also presented. |
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| AbstractList | Adaptive exponential functional link networks (AEFLN) are a type of linear-in-the-parameter nonlinear filters, which have shown enhanced modeling capability for nonlinear systems. However, the convergence speed of traditional AEFLN is generally slow for long impulse responses, hence, AEFLN trained using recursive least-squares becomes an attractive choice to achieve faster convergence. Moreover, huge computational burden of RLS makes it unsuitable in practical applications like echo cancellation. While low complexity versions of RLS are widely available in literature, they still suffer from low convergence speed. To address this issue, we propose a nonlinear acoustic echo cancellation (NAEC) system using AEFLN-RLS, based on the nearest Kronecker product (NKP) decomposition and low-rank approximation technique, which not only reduces computational complexity but also achieves improved convergence speed (especially tracking). To further improve the echo cancellation performance in non-stationary conditions, a variable regularization approach based NKP-AEFLN-RLS system is also proposed. To also reduce the computational complexity further, dichotomous coordinate descent (DCD) updates are incorporated into the proposed NKP-AEFLN-RLS NAEC system and its variable regularization version. Experimental results show the effectiveness of the proposed algorithms, with the variable regularized version of the algorithm using DCD iterations showing the best compromise between convergence capability and lower computational complexity.
•A NKPD based adaptive exponential functional link structure updated using the RLS is developed.•Near optimal VR scheme VR-NKP-AEFLN-RLS which updates the regularization parameter at each step.•Reduced computation complexity variant of proposed algorithms using a DCD approach is developed.•A detailed comparison of the computational complexity of proposed algorithms is also presented. |
| ArticleNumber | 109623 |
| Author | Christensen, Mads Græsbøll Patel, Vinal Jensen, Jesper Rindom Bhattacharjee, Sankha Subhra Benesty, Jacob |
| Author_xml | – sequence: 1 givenname: Vinal orcidid: 0000-0001-8804-5665 surname: Patel fullname: Patel, Vinal email: vp@iiitm.ac.in organization: Department of Electrical and Electronics Engineering, ABV-IIITM Gwalior, India – sequence: 2 givenname: Sankha Subhra orcidid: 0000-0002-7845-8113 surname: Bhattacharjee fullname: Bhattacharjee, Sankha Subhra email: ssbh@es.aau.dk organization: Department of Electronic Systems, Aalborg University, Denmark – sequence: 3 givenname: Jesper Rindom surname: Jensen fullname: Jensen, Jesper Rindom email: jrj@es.aau.dk organization: Department of Electronic Systems, Aalborg University, Denmark – sequence: 4 givenname: Mads Græsbøll surname: Christensen fullname: Christensen, Mads Græsbøll email: mgc@es.aau.dk organization: Department of Electronic Systems, Aalborg University, Denmark – sequence: 5 givenname: Jacob orcidid: 0000-0002-0036-5865 surname: Benesty fullname: Benesty, Jacob email: jacob.benesty@inrs.ca organization: INRS-EMT, University of Quebec, Montreal, Canada |
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| Cites_doi | 10.1016/j.sigpro.2017.01.009 10.1109/TNNLS.2017.2761259 10.1109/TASLP.2022.3161150 10.1109/TSA.2003.818077 10.1109/TASLP.2014.2324175 10.1109/TASLP.2021.3069089 10.1109/LSP.2007.910276 10.1109/TASLP.2018.2842146 10.1109/TASL.2010.2097251 10.1109/TASLP.2022.3202128 10.1109/ICASSP.2015.7178591 10.1109/TSP.2013.2258340 10.1016/j.sigpro.2012.08.013 10.1109/TASLP.2021.3084755 10.1121/1.2935769 10.1109/TASLP.2019.2903276 10.1016/j.sigpro.2021.108411 10.1109/TSP.2008.917874 10.1016/j.sigpro.2021.107984 10.1109/ISCAS.2018.8351345 10.1016/j.sigpro.2022.108726 10.23919/EUSIPCO.2018.8552955 10.1109/TASL.2013.2255276 10.1109/LSP.2008.2001559 10.1109/TCSI.2016.2572091 10.1049/el:20040353 10.1109/TCSI.2009.2015725 10.23919/EUSIPCO.2017.8081387 |
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| Keywords | Nonlinear echo cancellation Nearest Kronecker product Linear-in-the parameter nonlinear filter Low-rank approximation Recursive least-squares algorithm |
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