Adaptive regularized spectral reduction for stabilizing ill-conditioned bone-conducted speech signals
Bone-conducted (BC) speech signals are inherently challenging to analyze due to their wide frequency range, which leads to ill-conditioning in numerical analysis and linear prediction (LP) techniques. This ill-conditioning is primarily caused by the expansion of eigenvalues, which complicates the st...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-05-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2906.pdf |
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| author | Kanwar Muhammad Afaq Ammar Amjad Li-Chia Tai Hsien-Tsung Chang |
| author_facet | Kanwar Muhammad Afaq Ammar Amjad Li-Chia Tai Hsien-Tsung Chang |
| author_sort | Kanwar Muhammad Afaq |
| collection | DOAJ |
| description | Bone-conducted (BC) speech signals are inherently challenging to analyze due to their wide frequency range, which leads to ill-conditioning in numerical analysis and linear prediction (LP) techniques. This ill-conditioning is primarily caused by the expansion of eigenvalues, which complicates the stability and accuracy of traditional methods. To address this issue, we propose a novel regularized spectral reduction (RSR) method, built upon the regularized least squares (RLS) framework. The RSR method compresses the frequency range of BC speech signals, effectively reducing eigenvalue spread and enhancing the robustness of LP analysis. Key to the RSR approach is a regularization parameter, fine-tuned iteratively to achieve optimal performance. Experimental results demonstrate that RSR significantly outperforms existing techniques in eigenvalue compression, resulting in more accurate LP analysis for both synthetic and real BC speech datasets. These improvements hold promise for applications in hearing aids, voice recognition systems, and speaker identification in noisy environments, where reliable BC speech analysis is critical. |
| format | Article |
| id | doaj-art-af3bdef6694d49f9adeb13b2cd385e63 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-af3bdef6694d49f9adeb13b2cd385e632025-08-20T03:47:33ZengPeerJ Inc.PeerJ Computer Science2376-59922025-05-0111e290610.7717/peerj-cs.2906Adaptive regularized spectral reduction for stabilizing ill-conditioned bone-conducted speech signalsKanwar Muhammad Afaq0Ammar Amjad1Li-Chia Tai2Hsien-Tsung Chang3Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, TaiwanDepartment of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanDepartment of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanDepartment of Computer Science and Information Engineering, Chang Gung University, Taoyuan, TaiwanBone-conducted (BC) speech signals are inherently challenging to analyze due to their wide frequency range, which leads to ill-conditioning in numerical analysis and linear prediction (LP) techniques. This ill-conditioning is primarily caused by the expansion of eigenvalues, which complicates the stability and accuracy of traditional methods. To address this issue, we propose a novel regularized spectral reduction (RSR) method, built upon the regularized least squares (RLS) framework. The RSR method compresses the frequency range of BC speech signals, effectively reducing eigenvalue spread and enhancing the robustness of LP analysis. Key to the RSR approach is a regularization parameter, fine-tuned iteratively to achieve optimal performance. Experimental results demonstrate that RSR significantly outperforms existing techniques in eigenvalue compression, resulting in more accurate LP analysis for both synthetic and real BC speech datasets. These improvements hold promise for applications in hearing aids, voice recognition systems, and speaker identification in noisy environments, where reliable BC speech analysis is critical.https://peerj.com/articles/cs-2906.pdfSpectral compressionRegularization methodIll-conditioning improvementSpeech signal analysisBone-conducted voice signals |
| spellingShingle | Kanwar Muhammad Afaq Ammar Amjad Li-Chia Tai Hsien-Tsung Chang Adaptive regularized spectral reduction for stabilizing ill-conditioned bone-conducted speech signals PeerJ Computer Science Spectral compression Regularization method Ill-conditioning improvement Speech signal analysis Bone-conducted voice signals |
| title | Adaptive regularized spectral reduction for stabilizing ill-conditioned bone-conducted speech signals |
| title_full | Adaptive regularized spectral reduction for stabilizing ill-conditioned bone-conducted speech signals |
| title_fullStr | Adaptive regularized spectral reduction for stabilizing ill-conditioned bone-conducted speech signals |
| title_full_unstemmed | Adaptive regularized spectral reduction for stabilizing ill-conditioned bone-conducted speech signals |
| title_short | Adaptive regularized spectral reduction for stabilizing ill-conditioned bone-conducted speech signals |
| title_sort | adaptive regularized spectral reduction for stabilizing ill conditioned bone conducted speech signals |
| topic | Spectral compression Regularization method Ill-conditioning improvement Speech signal analysis Bone-conducted voice signals |
| url | https://peerj.com/articles/cs-2906.pdf |
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