Model częstotliwości podstawowej traktu głosowego zbudowany metodą grupowania argumentów (GMDH)

• Autorzy: Wiliński A. , Samborska-Owczarek A.
• Języki publikacji: PL

Abstrakty

EN

This paper concerns the problem of parameters estimation for a certain model, aiming at the approximation of output variable at the acceptable accuracy level. What distinguishes the way this common scientific task is here dealt with, is the usage of GMDH - Group Method of Data Handling (or more specifically the GMDH-based algorithm developed by the authors), which allows for simultaneous determination of both the structure and numerical characteristics of the model. The feature space under consideration is the matrix of repetitively observed attributes, describing the physical characteristics of voice samples, collected in order to determine the frequency of laryngeal tone for the purpose of medical diagnosis.

Słowa kluczowe

PL

metoda GMDH; aproksymacja; metoda grupowania argumentów; rozpoznawanie wzorców; funkcja spacji; przetwarzanie sygnału; właściwości traktu głosowego; ton krtaniowy; pozyskiwanie danych; badania naukowe

EN

approximation; GMDH; pattern recognition; feature space; signal processing; vocal tract quality; laryngeal tone; data mining; knowledge discovery

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Metody Informatyki Stosowanej
• Rocznik: 2007
• Tom: nr 2(Tom 12)
• Strony: 189--202
• Opis fizyczny: Bibliogr. 12 poz., rys., tab.

Twórcy

autor

Wiliński A.

autor

Samborska-Owczarek A.

• Politechnika Szczecińska, Wydział Informatyki

Bibliografia

• [1] Ivakhnenko A.G., Zaicenko J.P., Dimitrov V.D. Priniatije resenij na osnove samoorganizacji. Sovetskoe Radio, Moskwa 1976
• [2| Ivakhnenko A.G., Ivakhnenko G.A., Mueller J. Self-organization of Neural Network with Active Neurons. Pattern Recognition and Image Analysis, 1999 v.4 no.2
• [3] Tsymbal A., Pechenitzkiy M., Cunningham P. Diversity in Ensemble Feature Selection. Department of Computer Science, Trinity College, Dublin 2003.
• [4] Lemke F., Mueller .J. Self-organization Data Mining for a Portfolio Trading System. Journal of Computational Intelligence in Finance. 1997/05.
• [5] Hashem S., Schmeiser B. Improving Model Accuracy Using Optimal Linear Combinations of Trained Neural Networks. IEEE Transactions on Neural Networks, 1995.
• [6] Ivakhnenko A.G., Ivakhnenko G.A. Problems of Further Development of the Group Metod of Data Handling Algorithms. Pattern Recognition and Image Analysis, 2000 v.10 no.2
• [7] Parsa, V., Jamieson D.G. A comparison of high precision FO extraction algorithms for sustained vowels. Journal of Speech, Language, and Hearing Research. Vol. 42.1999, 1.
• [8] de Cheveigné A., Kawahara, II. Comparative evaluation of F0 estimation algorithms. Proceedings of EUROSPEECH-2001. Aalborg, Denmark, 2001
• [9] Gerhard D. Pitch Extraction and Fundamental Frequency:History and Current Techniąues. Technical Report TR-CS 2003-06. Regina, Dept. of Computer Science, University of Regina, 2003. ISSN 0828-3494.
• [10] Uppglrd S. Implementation and Analysis of Pitch Tracking Algorithms (Report for Master of Science Thesis Project). Stockholm, The Royal Institute of Technology, 2001.
• [11] Kay Elsmetrics Corporation. Disorder Voice Database Model 4337. Massachusetts Eye and Ear Infirmary Voice and Speech Lab, Boston, MA. 1994
• [12] Oppenheim A.V., Schafer R.W. From Freąuency to Quefrency: A History of the Cepstrum. IEEE Signal Processing Magazine, Vol. 21, Issue 5, 2004, s. 95-106