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.
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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
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DOI: https://doi.org/10.1007/978-3-642-39482-9_46
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