Leaf traits are commonly used in plant taxonomic applications. The aim of this study was to test the utility of fractal leaf parameters analysis (FA) and leaf red, green, and blue (RGB) intensity values based on support vector machines as a method for accurately discriminating Camellia (68 species from five sections, 11 from sect. Furfuracea, 13 from sect. Paracamellia, 15 from sect. Tuberculata, 24 from sect. Theopsis and 5 from sect. Camellia). The results showed that the best classification accuracy was up to 96.88% using the RBF SVM classifier (C = 16, g = 0.5). The linear kernel overall accuracy was 90.63%, and the correct classification rates of 40.63% and 93.75% were achieved for the sigmoid SVM classifier (C = 16, g = 0.5) and the polynomial SVM classifier (C = 16, g = 0.5, d = 2), respectively. A hierarchical dendrogram based on leaf FA and RGB intensity values was mostly on agreement with the generally accepted classification of the Camellia species. SVM combined with FA and RGB may be used for rapidly and accurately classifying Camellia species and identifying unknown genotypes.
Channel distortion and background noise often severely degrade the performance of automatic speaker recognition (ASR) system. In this paper, a new compensation method called glottal information-based cepstral mean subtraction (GIBCMS), which improves upon the conventional cepstral mean subtraction (CMS) method, is presented. Besides the cepstral information, GIBCMS has utilized the speaker’s glottal information, which also holds speaker-dependent characteristics, but is less vulnerable to environment than the cepstral one. In order to test its robustness under channel distortion, even with high level of background noise, we applied this method to the SRMC corpus which is added by noise at different SNRs. The experimental results show that GIBCMS gains better performance over other improved CMS methods on it.
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