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This paper proposes a new music genre classification algorithm based on dynamic music frame analysis and support vector machine (SVM). The dynamic music frame analysis could cover the long-term and the short-term music genre features which can represent the time-varying behavior of music signals. The music genre features used in this paper are mel-frequency cepstral coefficient (MFCC) and log energy...
As an effective tool in pattern recognition and machine learning, support vector machine (SVM) has been adopted abroad. In developing a successful SVM classifier, eliminating noise and extracting feature are very important. This paper proposes the application of kernel PCA to SVM for feature extraction. Then PSO Algorithm is adopted to optimization of these parameters in SVM. The novel time series...
Support vector machines (SVM) is a powerful new generation learning algorithm based on recent advances in statistical learning theory. Based on the principle of Structure Risk Minimization, Support Vector Machines have advantage than other classifier. SVM deliver state-of-the-art performance in real-word applications such as text categorization, hand-written character recognition, image classification,...
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