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A Flexible Framework for Audio Semantic Content Detection

机译:音频语义内容检测的灵活框架

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摘要

Audio semantic analysis is a crucial issue in multimedia applications. In this paper, a hierarchical framework is proposed for high-level semantic content detection for a continuous audio stream. In the proposed framework, basic audio events are modeled with hidden Markov models. Based on the obtained key audio event sequence, a neural network-based approach is proposed to discover the high-level semantic content of the audio context. With the neural network-based approach, human knowledge and machine learning are effectively combined in the semantic inference. We select some audio streams to evaluate the performance of the proposed framework, and the experiment results demonstrate the framework can achieve satisfying results.
机译:音频语义分析是多媒体应用程序中的关键问题。在本文中,提出了用于连续音频流的高级语义内容检测的分层框架。在提出的框架中,基本音频事件使用隐马尔可夫模型建模。基于获得的关键音频事件序列,提出了一种基于神经网络的方法来发现音频上下文的高级语义内容。使用基于神经网络的方法,可以将人类知识和机器学习有效地结合到语义推理中。我们选择了一些音频流来评估该框架的性能,实验结果表明该框架可以取得令人满意的结果。

著录项

  • 来源
  • 会议地点 Tainan(CT);eTainan(CT)
  • 作者

    Qi Li; Huadong Ma; Kanyan Zheng;

  • 作者单位

    Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China;

    Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China;

    Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算机网络;
  • 关键词

    high-level semantic; audio semantic; audio content analysis;

    机译:高级语义;音频语义;音频内容分析;

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