Semantic Context Detection Using Audio Event Fusion
Semantic Context Detection Using Audio Event Fusion
复制标题
使用音频事件融合进行语义上下文检测
DOI：
10.1155/asp/2006/27390
复制
发表时间：
2006
影响因子：
1.9
通讯作者：
Ja
中科院分区：
文献类型：
--
作者：
W. Chu;Wen;Ja
语义级内容分析是实现高效内容检索和管理的关键问题。我们提出了一个分层的方法，模型音频事件的时间序列，以完成语义上下文检测。两个层次的建模，音频事件和语义上下文建模，被设计为弥合物理音频特征和语义概念之间的差距。在这项工作中，隐马尔可夫模型（HMM）被用来模拟四个代表性的音频事件，即枪声，爆炸，引擎，和汽车刹车，在动作片。在语义上下文层次，生成（遍历隐马尔可夫模型）和判别（支持向量机（SVM））的方法进行了研究，融合音频事件之间的特征和相关性，这为检测枪战和汽车追逐场景提供了线索。实验结果验证了所提方法的有效性，并为利用音频特征进行信息挖掘提供了初步框架。
Semantic-level content analysis is a crucial issue in achieving efficient content retrieval and management. We propose a hierarchical approach that models audio events over a time series in order to accomplish semantic context detection. Two levels of modeling, audio event and semantic context modeling, are devised to bridge the gap between physical audio features and semantic concepts. In this work, hidden Markov models (HMMs) are used to model four representative audio events, that is, gunshot, explosion, engine, and car braking, in action movies. At the semantic context level, generative (ergodic hidden Markov model) and discriminative (support vector machine (SVM)) approaches are investigated to fuse the characteristics and correlations among audio events, which provide cues for detecting gunplay and car-chasing scenes. The experimental results demonstrate the effectiveness of the proposed approaches and provide a preliminary framework for information mining by using audio characteristics.