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Comparison of nonlinear dynamic methods and perturbation methods for voice analysis
Yu Zhang1, Jack J Jiang, Stephanie M Wallace
1Department of Surgery, Division of Otolaryngology Head and Neck Surgery, University of Wisconsin Medical School, Madison, Wisconsin 53792-7375, USA. zhang@surgery.wisc.edu
The Journal of the Acoustical Society of America
|November 4, 2005
Summary
Nonlinear dynamic methods, like correlation dimension, are more reliable for analyzing chaotic voice signals than perturbation methods, especially with shorter or noisy data. This offers a more stable approach for assessing voice disorders.
Area of Science:
- Acoustics and Signal Processing
- Nonlinear Dynamics
- Biomedical Engineering
Background:
- Perturbation methods are commonly used for voice analysis but have limitations.
- Chaotic signals in voice present challenges for traditional analysis techniques.
- Understanding the impact of signal parameters on analysis reliability is crucial.
Purpose of the Study:
- To compare nonlinear dynamic methods and perturbation methods for voice signal analysis.
- To evaluate the influence of signal length, sampling rate, and noise on these methods.
- To determine the most robust method for quantifying chaotic and nearly periodic voice signals.
Main Methods:
- Theoretical and experimental comparison of nonlinear dynamic methods (e.g., correlation dimension) and perturbation methods.
- Analysis of voice samples with varying signal lengths, sampling rates, and noise levels.
- Assessment of pitch tracking and sensitivity to initial conditions for perturbation methods.
Main Results:
- Perturbation methods are unreliable for chaotic voice signals due to pitch tracking difficulties and sensitivity to initial states.
- Nonlinear dynamic methods, specifically correlation dimension, can quantify chaotic time series effectively.
- Correlation dimension analysis is more stable for nearly periodic voice samples under adverse conditions (shorter length, lower sampling rate, higher noise).
Conclusions:
- Nonlinear dynamic methods, particularly correlation dimension, offer a more robust and stable approach to voice signal analysis compared to perturbation methods.
- The correlation dimension method overcomes limitations of perturbation analyses, improving objective assessment of voice disorders.
- This method has potential for real-time analysis and cost reduction in experimental voice disorder assessments.