Mixed type audio classification with Support Vector Machine

Abstract

Content-based classification of audio data is an important problem for various applications such as overall analysis of audio-visual streams, boundary detection of video story segment, extraction of speech segments from video, and content-based video retrieval. Though the classification of audio into single type such as music, speech, environmental sound and silence is well studied, classification of mixed type audio data, such as clips having speech with music as background, is still considered a difficult problem. In this paper, we present a mixed type audio classification system based on Support Vector Machine (SVM). In order to capture characteristics of different types of audio data, besides selecting audio features, we also design four different representation formats for each feature. Our SVM-based audio classifier can classify audio data into five types: music, speech, environment sound, speech mixed with music, and music mixed with environment sound. The experimental results show that our system outperforms other classification systems using k Nearest Neighbor (k-NN), Neural Network (NN), and Naive Bayes (NB).