基于支持向量机的音频分类与分割

音频分类与分割是提取音频结构和内容语义的重要手段，是基于内容的音频、视频检索和分析的基础。支持向量机(SVM)是一种有效的统计学习方法。本文提出了一种基于SVM的音频分类算法。将音频分为5类：静音、噪音、音乐、纯语音和带背景音的语音。在分类的基础上，采用3个平滑规则对分类结果进行平滑。分析了SVM分类嚣的分类性能，同时也评估了本文提出的新的音频特征在SVM分类嚣上的分类效果。实验结果显示，基于SVM的音频分类算法分类效果良好，平滑处理后的音频分割结果比较准确。

Audio Classification and Segmentation Based on Support Vector Machines

Audio classification and segmentation are an important access to extract audio structure and content, and are a basis for further audio/video retrieval and analysis. Support vector machines (SVM) is a valid statistic learning method. In this paper, the work on audio classification based on SVM is presented. Five audio classes are considered in this paper: silence, noise, music, pure speech and speech over background sound. Three smooth rules are present- ed and applied in the final segmentation. The performance of SVM on audio classification is evaluated. The effective- ness of some new proposed features is also evaluated. Experiment results show that SVM performs very well for au- dio classification and segmentation accuracy is good with the proposed three smooth rules.