SVM-based audio scene classification

Audio scene classification is very important in audio indexing, retrieval and video content analysis. In this paper we present our approach that uses support vector machine (SVM) for audio scene classification, which classifies audio clips into one of five classes: pure speech, non-pure speech, music, environment sound, and silence. Among of them, non-pure speech may further be divided into speech with music and speech with noise. We also describe two methods to select effective and robust audio feature sets. Based on these feature sets, we have evaluated and compared the performance of two kinds of classification frameworks on a testing database that is composed of about 4-hour audio data. The experimental results have shown that the SVM-based method yields high accuracy with high processing speed.