Hierarchical audio classification algorithm for news video content analysis
面向新闻视频内容分析的音频分层分类算法*

This paper proposed hierarchical audio classification algorithm, which first classified the news audio stream into silence, speech and music with rule-based classifier, and then employed hidden Markov models to categorize the speech and music to male-anchor speech, female-anchor speech, alternate speech, monologue speech, live report and music. The experiment results show that the classification works best in male-anchor speech,female-anchor speech and music, in which precision and reall can both reach more than 90%. The classification performs worst in alternate speech with precision of 57.5% and with recall of 79.3%. The performance of classification in other types is at the average level with precision and recall ranging from 70% to 90%. Compared with the other representative algorithm, this method works well with relatively high precision.