International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 5, No. 9, 8 Jul. 2013
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A Hybrid of Genetic Algorithm and Support Vector Machine for Feature Reduction and Detection of Vocal Fold Pathology
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Author(s)
Index Terms
Vocal Fold Pathology Diagnosis, Wavelet Packet Decomposition (WPD), Mel-Frequency-Cepstral-Coefficient (MFCC), Principal Component Analysis (PCA), Genetic Algorithm (GA), Support Vector Machine (SVM)
Abstract
Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches and algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods (Support Vector Machines, Artificial Neural Networks, etc), the first and second stages play a critical role in performance and accuracy of the classification system. In this paper we present initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also a new GA-based method for feature reduction stage is proposed and compared with conventional methods such as Principal Component Analysis (PCA). Support vector machine is used as a classifier for evaluating the performance of the proposed method. The results show the priority of the proposed method in comparison with the current methods.
Cite This Paper
Vahid Majidnezhad,Igor Kheidorov,"A Hybrid of Genetic Algorithm and Support Vector Machine for Feature Reduction and Detection of Vocal Fold Pathology", IJIGSP, vol.5, no.9, pp.1-7, 2013. DOI: 10.5815/ijigsp.2013.09.01
Reference
[1]J.B. Alonso, J.D. Leon, I. Alonso, M.A. Ferrer, "Automatic Detection of Pathologies in the Voice by HOS Based Parameters". EURASIP Journal on Applied Signal Processing, 2001(4): 275-284, 2001.
[2]L.G. Ceballos, J. Hansen, J. Kaiser, "A Non-Linear Based Speech Feature Analysis Method with Application to Vocal Fold Pathology Assessment". IEEE Trans. Biomedical Engineering, 45(3): 300-313, 2005.
[3]L.G. Ceballos, J. Hansen, J. Kaiser, "Vocal Fold Pathology Assessment Using AM Autocorrelation Analysis of the Teager Energy Operator". ICSLP-1996 Proc., pp: 757-760, 1996.
[4]C. Adnene, B. Lamia, "Analysis of Pathological Voices by Speech Processing". Signal Processing and Its Applications, 2003 Proc., 1(1): 365-367, 2003.
[5]C. Manfredi, "Adaptive Noise Energy Estimation in Pathological Speech Signals". IEEE Trans. Biomedical Engineering, 47(11): 1538-1543, 2000.
[6]J.I.G. Llorente, P.G. Vilda, "Automatic Detection of Voice Impairments by Means of Short-Term Cepstral Parameters and Neural Network Based Detectors". IEEE Trans. Biomedical Engineering, 51(2): 380-384, 2004.
[7]M.D.O. Rosa, J.C. Pereira, M. Grellet, "Adaptive Estimation of Residue Signal for Voice Pathology Diagnosis". IEEE Trans. Biomedical Engineering, 47(1): 96-104, 2000.
[8]V. Majidnezhad, I. Kheidorov, "A Novel Method for Feature Extraction in Vocal Fold Pathology Diagnosis". Proceeding of the 3rd International Conference on Wireless Mobile Communication and Healthcare, LNICST 61, pp: 96-105, 2013.
[9]V. Majidnezhad, I. Kheidorov, "A HMM-Based Method for Vocal Fold Pathology Diagnosis". IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 6, No 2. pp: 135-138, 2012.
[10]W. Chen, C. Peng, X. Zhu, B. Wan, D. Wei, "SVM-based identification of pathological voices". Proceedings of the 29th Annual International Conference of the IEEE EMBS. pp: 3786-3789, 2007.
[11]P. Go´mez, F. Dı´az, A. A´lvarez, K. Murphy, C. Lazaro, R. Martinez, V. Rodellar, "Principal component analysis of spectral perturbation parameters for voice pathology detection". Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems, pp: 41-46, 2005.
[12]D. Michaelis, M. Frohlich, H.W. Strube, "Selection and combination of acoustic features for the description of pathologic voices". Journal of the Acoustical Society of America, 103(3): 1628-1639, 1998.
[13]M. Marinaki, C. Kotropoulos, I. Pitas, N. Maglaveras, "Automatic detection of vocal fold paralysis and edema". Proceedings of Eighth International Conference on Spoken Language Processing-ICSLP. pp: 31-32, 2004.
[14]J.I.G. Llorente, P.G. Vilda, M.B. Velasco, "Dimensionality Reduction of a Pathological Voice Quality Assessment System Based on Gaussian Mixture Models and Short-Term Cepstral Parameters". IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 53(10): 1943-1953, 2006.
[15]R.T. Ritchings, M.A. McGillion, C.J. Moore, "Pathological voice quality assessment using artificial neural networks". Medical Engineering & Physics, 24(8): 561-564, 2002.
[16]H.K. Herisa, B.S. Aghazadeh, M.N. Bahrami, "Optimal feature selection for the assessment of vocal fold disorders". Computers in Biology and Medicine, 39(10): 860-868, 2009.
[17]E. Fonseca, R.C. Guido, J.C. Pereira, P.R. Scalassarsa, C.D. Maciel, J.C. Pereira, "Wavelet time frequency analysis and least squares support vector machines for identification of voice disorders". Computers in Biology and Medicine, 37(4): 571-578, 2007.
[18]R.C. Guido, J.C. Pereira, E. Fonseca, F.L. Sanchez, L.S. Vierira, "Trying different wavelets on the search for voice disorders sorting". Proceedings of the 37th IEEE International Southeastern Symposium on System Theory, pp: 495-499, 2005.
[19]K. Umapathy, S. Krishnan, "Feature analysis of pathological speech signals using local discriminant bases technique". Medical and Biological Engineering and Computing, 43(4): 457-464, 2005.
[20]M.K. Arjmandi, M. Pooyan, "An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine". Biomedical Signal Processing and Control, 7(1): 3-19, 2012.
[21]V. Majidnezhad, H.M. Gader, E. Efimov, "A Novel Hybrid Algorithm for Task Graph Scheduling". International Journal of Computer Science Issues, Vol. 8, Issue 2, pp: 32-38, 2011.
[22]V.N. Vapnik, "Statistical Learning Theory". New York, Wiley, 1998.
[23]T. Li, M. Oginara, Q. Li, "A comparative study on content based music genre classification". Proc. Of the 26th annual int.ACM SIGIR conf. on Research and development in information retrieval, pp: 282–289, 2003.
[24]R. Kohavi, G. John, "Wrappers for feature subset selection". Artificial Intelligence, 97(1-2): 272-324, 1997.