Audio classification based on one-class SVM
基于单类支持向量机的音频分类

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).