KaydedilenlerDerginizi ekleyin

Dergiler / Electrica / 2018 / Cilt: 18 - Sayı: 1

A New Approach for Detection of Pathological Voice Disorders with Reduced Parameters

HAYDAR ANKIŞHAN
Sayfa
60–71
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Resultantly, the process complexity decreased and the system speed increased.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Voice data has demonstrated chaotic behavior in previous studies.
📊
Temel Bulgular & Veri:Therefore, studying the linear properties alone does not yield successful results.
💡
Sonuç & Katkı:Resultantly, the process complexity decreased and the system speed increased.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Özet

Voice data has demonstrated chaotic behavior in previous studies. Therefore, studying the linear properties alone does not yield successful results. This is valid for the examination of voice data as well. Therefore, conducting studies including chaotic features as well as existing technologies is inevitable. The main purpose of this study is to detect voice pathologies with fewer special features using new chaotic features. Both linear and nonlinear characteristics were used in this study. In this context, the largest Lyapunov exponents and entropy are preferred as chaotic properties because of their success in previous studies. Very few results with 100% accuracy were obtained in the experimental studies. In this study, multiple support vector machines (SVMs) were selected as a classifier because of their success in previous similar data types. Thus, the desired accuracy level was achieved using fewer features. Resultantly, the process complexity decreased and the system speed increased.

🔖

Bu Makaleye Atıf Yap

ANKIŞHAN, H. (2018). A New Approach for Detection of Pathological Voice Disorders with Reduced Parameters. Electrica, Cilt: 18 - Sayı: 1, 60–71. https://ajindex.com/articles/153728

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (4)

  1. J. R. O. Arroyave, J.F.V. Bonilla, E. D. Trejos, “Acoustic analysis and non linear dynamics applied to voice pathology detection: A review”, Recent Patents on Signal Processing, vol. 2, pp. 1–11, 2012.
  2. S. Fong, K. Lan, R. Wong, “Classifying human voices by using hybrid SFX time-series preprocessing and ensemble feature selection”, BioMed Research International, vol. 1–27, 2013.
  3. J. I. Godino-Llorente, N. S´aenz-Lech´on, V. Osma-Ruiz, S.Aguilera-Navarro, P. G´omez- Vilda, “An integrated tool for the diagnosis of voice disorders,” Medical Engineering & Physics, vol. 28, pp. 276–289, 2006 [CrossRef] C. Manfredi, M. D’Aniello, P. Bruscaglioni, A. Ismaelli, “A comparative analysis of fundamental frequency estimation methods with application to pathological voices,” Medical Engineering & Physics, vol. 22, pp. 135–147, 2000. [CrossRef] M. Farrus, J. Hernando, P. Ejarque, “Jitter and shimmer measurements for speaker Recognition,” Annual Conference of the International Speech Communication Association (Interspeech 2007),
  4. Antwerp, Belgium, pp. 778–781, 2007. P. Lieberman, “Some acoustic measures of the fundamental periodicity of normal and pathologic larynges”, The Journal of the Acoustical Society of America, vol. 35, pp. 344–353, 1963. [CrossRef] Y. Horii, “Vocal shimmer in sustained phonation,” Journal of Speech, Language, and Hearing Research, vol. 23, pp. 202–209, 1980. [CrossRef] E. Yumoto, W. J. Gould, T. Baer, “Harmonics-to-noise ratio as an index of the degree of hoarseness,” The Journal of the Acoustical Society of America, vol. 71, pp: 1544–1550, 1982. [CrossRef] L. R. Rabiner and B. H. Juang, “Fundamentals of Speech Recognition,” Vol. 14, PTR Prentice Hall, Englewood Cliffs, NJ. J. I. Godino-Llorente, P. Gomez-Vilda “Automatic detection of voice impairmentsby means of short-term cepstral parameters and neural network based detectors,” IEEE Trans. Biomed. Eng., vol. 51, pp. 380–384, 2004. [CrossRef] Y. Zhang, et al. “Nonlinear dynamic analysis of voices before and after surgicalexcision of vocal polyps,” J. Acoust. Soc. Am., vol. 115, pp. 2270–2277, 2004. [CrossRef] T. L. Eadie, P. C. Doyle, “Classification of dysphonic voice: Acoustic and auditory-perceptual measures,” Journal of Voice, vol. 19, pp. 1–14, 2005. [CrossRef] S. Hadjitodorov and P. Mitev, “A computer system for acoustic analysis of pathological voices and laryngeal diseases screening,” Medical Engineering & Physics, vol. 24, p. 419–429, 2002. [CrossRef] N. Saenz-Lechon, J. I. Godino-Llorent