Hierarchical modeling using automated sub-clustering for sound event recognition
Hierarchical modeling using automated sub-clustering for sound event recognition
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DOI：
10.1109/waspaa.2013.6701862
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
M. Niessen;T. V. Kasteren;A. Merentitis
中科院分区：
文献类型：
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作者：
M. Niessen;T. V. Kasteren;A. Merentitis
声音事件的自动识别允许在诸如安全、移动的和多媒体等领域中的新颖应用。在这项工作中，我们提出了一个层次化的隐马尔可夫模型的声音事件检测，自动集群的固有结构的事件到子事件。我们评估我们的方法在IEEE音频挑战数据集组成的办公室声音事件，并提供了一个系统的比较我们的方法的各种构建块，以证明在模型中纳入某些依赖关系的有效性。层次隐马尔可夫模型在用于评估挑战提交的测试数据集上实现了45.5%的平均基于帧的F度量识别性能。我们还展示了如何将分层模型用作元分类器，尽管在特定的应用程序中，这并没有导致测试数据集的性能提高。
The automatic recognition of sound events allows for novel applications in areas such as security, mobile and multimedia. In this work we present a hierarchical hidden Markov model for sound event detection that automatically clusters the inherent structure of the events into sub-events. We evaluate our approach on an IEEE audio challenge dataset consisting of office sound events and provide a systematic comparison of the various building blocks of our approach to demonstrate the effectiveness of incorporating certain dependencies in the model. The hierarchical hidden Markov model achieves an average frame-based F-measure recognition performance of 45.5% on a test dataset that was used to evaluate challenge submissions. We also show how the hierarchical model can be used as a meta-classifier, although in the particular application this did not lead to an increase in performance on the test dataset.