Loading [a11y]/accessibility-menu.js
Voice pathology detection with predictable component analysis and wavelet decomposition model | IEEE Conference Publication | IEEE Xplore

Voice pathology detection with predictable component analysis and wavelet decomposition model


Abstract:

In this study, we present a first attempt to apply the predictable component analysis to voice signals. This method projects the signals in components that optimize the n...Show More
Notes: As originally published there was an error in the document. The file originally submitted to IEEE Xplore was missing half of a page. The authors have now provided a corrected PDF file.

Abstract:

In this study, we present a first attempt to apply the predictable component analysis to voice signals. This method projects the signals in components that optimize the normalized error variance, a quantity related to the predictability of the signals. Using the proposed method along with a wavelet decomposition model, we obtain voice signals estimations. These voice signals are sustained vowel "a" from three groups of people: those with healthy larynxes and others with nodule or Reinke's edema on the vocal folds. Using the Shannon entropy of the estimation errors, we present evidences that it is possible to detect the pathological cases using this measure.
Notes: As originally published there was an error in the document. The file originally submitted to IEEE Xplore was missing half of a page. The authors have now provided a corrected PDF file.
Date of Conference: 16-20 October 2011
Date Added to IEEE Xplore: 01 December 2011
ISBN Information:
Conference Location: Paraty, Brazil

References

References is not available for this document.