Abstract: The author studied an audio classification method based on One-Class Support Vector Machine (OCSVM), which could form a decision function for every single class sample and accordingly obtain the aim of classification based on maximum of decision function. By employing wavelet packed transformation to extract features of audio and integrating multiple features, five audio classes were made: pure speech, music, environmental sound, speech over background and silence. Experimental results show that OCSVM has better classification accuracy, and performs better than the other classification systems using the Bayes, Hidden Markov Model (HMM) and Neural Network (NN).

Key words: wavelet

摘要： 研究一种基于单类支持向量机的音频分类方法，能够使每一类样本都独立地获得一个决策函数,通过决策函数的最大值来判断样本所属的类。通过使用小波包变换提取语音特征向量，并融合多特征向量，将音频分为5类：纯语音、音乐、环境音、含背景音语音和静音。实验结果表明这种方法具有较好的分类精度，性能优于贝叶斯、隐马尔可夫模型和神经网络分类器。

关键词: 单类支持向量机, 音频分类, 特征提取, 小波, One-Class Support Vector Machine (OCSVM), audio classification, character extraction