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Efficient Multiple Kernel Support Vector Machine Based Voice Activity Detection

2011; Institute of Electrical and Electronics Engineers; Volume: 18; Issue: 8 Linguagem: Inglês

10.1109/lsp.2011.2159374

1558-2361

Ji Wu, Xiao-Lei Zhang,

Music and Audio Processing

In this letter, we propose a multiple kernel support vector machine (MK-SVM) method for multiple feature based VAD. To make the MK-SVM based VAD practical, we adapt the multiple kernel learning (MKL) thought to an efficient cutting-plane structural SVM solver. We further discuss the performances of the MK-SVM with two different optimization objectives, in terms of minimum classification errors (MCE) and improvement of receiver operating characteristic (ROC) curves. Our experimental results show that the proposed method not only leads to better global performances by taking the advantages of multiple features but also has a low computational complexity.