Fine-tuning SVM for Enhancing Speech/Music Classification

Support vector machines have been extensively studied and utilized in pattern recognition area for years. One of interesting applications of this technique is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel approach that improves the speech/music classification of support vector machines. 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. We first analyze the impact of kernel width parameter on the classifications made by support vector machines. From this analysis, we observe that we can fine-tune outputs of support vector machines with the kernel width parameter. To make the most of this capability, we identify strong correlation among neighboring input frames, and use this correlation information as a guide to adjusting kernel width parameter. According to the experimental results, the proposed algorithm is found to have potential for improving the performance of support vector machines.