Generative and Discriminative Modeling toward Semantic Context Detection in Audio Tracks

W. Chu, Wen-Huang Cheng, Ja-Ling Wu
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引用次数: 14

Abstract

Semantic-level content analysis is a crucial issue to achieve efficient content retrieval and management. We propose a hierarchical approach that models the statistical characteristics of several audio events over a time series to accomplish semantic context detection. Two stages, including audio event and semantic context modeling/testing, are devised to bridge the semantic gap between physical audio features and semantic concepts. For action movies we focused in this work, hidden Markov models (HMMs) are used to model four representative audio events, i.e. gunshot, explosion, car-braking, and engine sounds. 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 various audio events, which provide cues for detecting gunplay and car-chasing scenes. The experimental results demonstrate the effectiveness of the proposed approaches and draw a sketch for semantic indexing and retrieval. Moreover, the differences between two fusion schemes are discussed to be the reference for future research.
基于生成和判别建模的音轨语义上下文检测
语义级内容分析是实现高效内容检索和管理的关键问题。我们提出了一种分层方法,该方法在时间序列上对几个音频事件的统计特征进行建模,以完成语义上下文检测。设计了两个阶段,包括音频事件和语义上下文建模/测试,以弥合物理音频特征和语义概念之间的语义差距。对于我们在这项工作中关注的动作电影,隐马尔可夫模型(hmm)用于建模四种代表性的音频事件,即枪击,爆炸,汽车制动和发动机声音。在语义上下文层面,研究了生成式(遍历隐马尔可夫模型)和判别式(支持向量机,SVM)方法来融合各种音频事件之间的特征和相关性,为检测枪战和追车场景提供线索。实验结果证明了所提方法的有效性,并为语义索引和检索绘制了草图。讨论了两种融合方案之间的差异,为今后的研究提供参考。
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