Abstract
Research on the human health evaluation through sound analysis is now attracting more and more researchers in the world. Acoustic analysis could be a useful tool to diagnose the disease. Therefore, pathological voices can be used to evaluate the health status as a complementary technique, such as bronchitis. In this article, we proposed a nonlinear dynamic method to analysis pathological voices. Firstly, pathological voices were preprocessed and numerous features were extracted. Secondly, a binary coded chromosome genetic algorithm (GA) was applied as feature selection method to optimize feature descriptor set. The experimental results show that GA, PCA along with support vector machine (SVM) has the best performance in the pathology voices diagnosis.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fang, C.Y., Li, H.F., Ma, L., Hong, W.X.: Status and Development of Human Health Evaluation Based on Sound Analysis. In: First International Conference on Cellar Molecular Biology Biophysics and Bioengineering, p.66 (2010)
Fang, C.Y., Li, H.F.: Sound Analysis for Diagnosis of Children Health Based on MFCCE and GMM. In: International Review on Computers and Software, pp. 1153–1156 (2011)
Parsa, V., Jamieson, D.G.: Interactions Between Speech Coders and Disordered Speech. Speech Commun. 40, 365–385 (2003)
Hadjitodorov, S., Mitev, P.: A Computer System for Acoustic Analysis of Pathological Voices And Laryngeal Diseases Screening. Med. Eng. Phys. 24, 419–429 (2002)
Godino-Llorente, J.I., Gomez-Vilda, P.: Automatic Detection of Voice Impairments by Means of Short-term Cepstral Parameters and Neural Network Based Detectors. IEEE Trans. Biomed. Eng. 51, 380–384 (2004)
Godino-Lorente, J.I., Gomez-Vilda, P., Blanco-Velasco, M.: Dimensionality Reduction of A Pathological Voice Quality Assessment System Based on Gaussian Mixture Models And Short-term Cepstral Parameters. IEEE Trans. Biomed. Eng. 53, 1943–1953 (2006)
Arjmandi, M.K.: Identifies Voice Disorders Using Long-time Features And Support Vector Machine With Different Feature Reduction Methods. Journal of Voice (2011)
Nayak, J., Bhat, P.S.: Identification of voice disorders using speech samples. IEEE Trans. 37, 951–953 (2003)
Fonseca, E.S., Gudio, R.C., Scalassara, P.R.: Wavelet Time-frequency Analysis And Least Squares Support Vector Machines Forthe Identification of Voice Disorders. Comput. Biol. Med. 37, 571–578 (2007)
Fu, W.J., Yang, X.H., Wang, Y.T.: Heart Sound Diagnosis Based on DTW and MFCC. In: 2010 3rd International Congress on Image and Signal Processing, p. 2920 (2010)
Cohen, A., Landsberg, D.: Analysis And Automatic Classification of Breath Sounds. IEEE Transactions on Biomedical Engineering 31, 585–590 (1984)
Anderson, K., Qiu, Y.H., Arthur, R.: Whittaker:Breath Sounds Asthma And The Mobile Phone. The Lancet 358(9290), 1343–1344 (2001)
Gavriely, N., Airflow, D.W.: Effects on Amplitude And Spectral Content of Normal Breath Sounds. Journal of Applied Physiology 80(1), 5–13 (1996)
Peng, H.C., Long, F.H., Ding, C.: Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance,and Min-Redundancy. IEEE Trans. Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)
Yang, J., Ames, I.A., Honavar, V.: Feature Subset Selection Using A Genetic Algorithm. Intelligent Systems and Their Applications, 44–49 (1998)
Ren, J., Qiu, Z., Fan, W., Cheng, H., Yu, P.S.: Forward semi-supervised feature selection. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 970–976. Springer, Heidelberg (2008)
Bu, H.L., Zheng, S.Z., Xia, J.: Genetic Algorithm Based Semi-feature Selection Method. In: 2009 International Joint Conference on Bioinformatics Systems Biology and Intelligent Computing, pp. 521–524 (2009)
Oh, I.S., Lee, J.S., Moon, B.R.: Hybrid Genetic Algorithms for Feature Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11), 1424–1437 (2004)
Eyben, F., Wóllmer, M., Schuller, B.: OpenSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor. In: Proc. ACM Multimedia, pp. 1459–1462. ACM, Florence (2010)
The INTERSPEECH 2012 Speaker Trait Challenge, http://emotion-research.net/sigs/speech-sig/is12-speaker-trait-challenge
Guo, D.M., Zhang, D., Li, N.M., Zhang, L., Yang, J.H.: A Novel Breath Analysis System Based on Electronic Olfaction. IEEE Trans. Biomedical Engineering 57(11), 2753–2760 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chunying, F., Haifeng, L., Lin, M., Xiaopeng, Z. (2013). Nonlinear Dynamic Analysis of Pathological Voices. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-39482-9_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39481-2
Online ISBN: 978-3-642-39482-9
eBook Packages: Computer ScienceComputer Science (R0)