A HIERARCHICAL APPROACH FOR AUDIO STREAM SEGMENTATION AND CLASSIFICATION

Abstract

This paper describes a hierarchical approach for fast audio stream segmentation and classification. With this approach, the audio stream is firstly segmented into audio clips by MBCR (Multiple sub-Bands spectrum Centroid relative Ratio) based histogram modeling. Then a MGM (Modified Gaussian modeling) based hierarchical classifier is adopted to put the segmented audio clips into six pre-defined categories in terms of discriminative background sounds, which is pure speech, pure music, song, speech with music, speech with noise and silence. The experiments on real TV program recordings showed that this approach has higher accuracy and recall rate for audio classification with a fast speed under noise environments