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Related Experiment Video

Updated: Sep 18, 2025

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
04:32

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Published on: December 20, 2024

440

Adaptive regularized spectral reduction for stabilizing ill-conditioned bone-conducted speech signals.

Kanwar Muhammad Afaq1, Ammar Amjad2, Li-Chia Tai2

  • 1Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

A new regularized spectral reduction (RSR) method improves bone-conducted (BC) speech analysis by reducing frequency range and enhancing linear prediction (LP) accuracy. This technique offers better performance for BC speech processing in challenging environments.

Keywords:
Bone-conducted voice signalsIll-conditioning improvementRegularization methodSpectral compressionSpeech signal analysis

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Area of Science:

  • Signal Processing
  • Acoustics
  • Biomedical Engineering

Background:

  • Bone-conducted (BC) speech signals present analysis challenges due to wide frequency ranges, causing ill-conditioning in numerical and linear prediction (LP) methods.
  • Eigenvalue spread in BC speech signals complicates the stability and accuracy of traditional analysis techniques.

Purpose of the Study:

  • To introduce a novel regularized spectral reduction (RSR) method for improved BC speech signal analysis.
  • To enhance the robustness and accuracy of linear prediction (LP) for BC speech by addressing eigenvalue spread.

Main Methods:

  • Developed a regularized spectral reduction (RSR) method based on the regularized least squares (RLS) framework.
  • Implemented iterative fine-tuning of a regularization parameter within the RSR method.
  • Compressed the frequency range of BC speech signals to reduce eigenvalue spread.

Main Results:

  • The RSR method demonstrated superior eigenvalue compression compared to existing techniques.
  • Significantly improved the accuracy of linear prediction (LP) analysis for both synthetic and real BC speech data.
  • Validated the effectiveness of RSR in enhancing the robustness of BC speech analysis.

Conclusions:

  • The proposed RSR method effectively overcomes the ill-conditioning issues in BC speech analysis.
  • RSR offers a more accurate and robust approach to LP analysis of BC speech signals.
  • This advancement has potential applications in hearing aids, voice recognition, and speaker identification systems, particularly in noisy conditions.