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Improving SVM with Second-Order Conditional MAP for Speech/Music Classification  

Lim, Chung-Soo (Mokpo National University)
Chang, Joon-Hyuk (Dep. of Electronic Engineering, Hanyang University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.48, no.5, 2011 , pp. 102-108 More about this Journal
Abstract
Support vector machines are well known for their outstanding performance in pattern recognition fields. One example of their applications is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel scheme that improves the speech/music classification of support vector machines based on the second-order conditional maximum a priori. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. According to experimental results, the proposed algorithm shows its compatibility and potential for improving the performance of support vector machines.
Keywords
Second-order Conditional Maximum a posteriori (Second-order CMAP); Support Vector Machine (SVM); Selectable Mode Vocoder (SMV); Speech/Music Classification Algorithm;
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Times Cited By KSCI : 1  (Citation Analysis)
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