| Abstract: |
| This talk presents the application of wavelet theory in the analysis of voice pathologies, focusing on its advantages for handling non-stationary biomedical signals. Voice disorders, such as Reinke`s Edema and Dysphonia, exhibit complex patterns that can be effectively characterized using wavelet-based methods. We explore multiresolution wavelet analysis to dissect voice signals into frequency components, enabling a detailed examination of time-frequency characteristics. This approach facilitates the identification of pathological indicators that are otherwise challenging to detect with traditional methods. By employing specific wavelet filters, including Daubechies and Symlets, we demonstrate the capacity to isolate crucial features, such as irregular vibratory patterns and spectral shifts, providing valuable insights into the underlying pathological conditions. The application of wavelet analysis enhances diagnostic accuracy and provides a robust framework for understanding the dynamics of voice disorders. This talk aims to highlight recent advancements, challenges, and opportunities in using wavelets for the non-invasive evaluation of vocal health, promoting interdisciplinary collaboration between mathematics, biomedical engineering, and clinical practice. |
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