Broadcast News Segmentation By Audio Type Analysis
Tin Lay Nwe, Haizhou Li · 2006
It is common for us to define audio types according to human perception instead of audio spectral properties. In this paper, we analyse the spectral properties of audio types and propose the acoustic features based on spectral properties and harmonic enhancement, to classify audio. By analyzing the spectral properties of sound types, a multi-model HMM is proposed to integrate the primitive spectral properties in statistical modeling. To validate the approach, we build a classifier to segment audio streams into speech, commercials, environmental sound, physical violence and silence in multiple steps. It is shown that the proposed approach outperforms conventional methods. Experimental evaluations on 20 audio tracks of the TRECVID broadcast news database have shown the effectiveness of the proposed approach.