Sound Event Detection Using Graph Laplacian Regularization Based on Event Co-occurrence
Sound Event Detection Using Graph Laplacian Regularization Based on Event Co-occurrence
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DOI：
10.1109/icassp.2019.8683708
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Keisuke Imoto;Seisuke Kyochi
中科院分区：
文献类型：
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作者：
Keisuke Imoto;Seisuke Kyochi
某种情况下发生的声音事件的类型是有限的，并且某些声音事件很可能同时发生；例如，“菜肴”和“玻璃叮当作响”。在本文中，我们提出了一种利用图拉普拉斯正则化并考虑声音事件共现的声音事件检测技术。在所提出的方法中，声音事件的发生被表示为图，其节点指示事件发生的频率，其边指示声音事件的共现。然后将该图形表示用于声音事件建模，该建模在具有考虑图形结构的正则化项的目标函数下进行优化。使用TUT Sound Events 2016开发、2017开发和TUT Acoustic Scenes 2016开发获得的实验结果表明，在基于分段的F1分数方面，与传统的基于CNN-BiGRU的方法相比，该方法将声音事件的检测性能提高了7.9个百分点。此外，结果表明，所提出的方法可以比传统方法更准确地检测同时发生的声音事件。
The types of sound events that occur in a situation are limited, and some sound events are likely to co-occur; for instance, "dishes" and "glass jingling." In this paper, we propose a technique of sound event detection utilizing graph Laplacian regularization taking the sound event co-occurrence into account. In the proposed method, sound event occurrences are represented as a graph whose nodes indicate the frequency of event occurrence and whose edges indicate the co-occurrence of sound events. This graph representation is then utilized for sound event modeling, which is optimized under an objective function with a regularization term considering the graph structure. Experimental results obtained using TUT Sound Events 2016 development, 2017 development, and TUT Acoustic Scenes 2016 development indicate that the proposed method improves the detection performance of sound events by 7.9 percentage points compared to that of the conventional CNN-BiGRU-based method in terms of the segment-based F1-score. Moreover, the results show that the proposed method can detect co-occurring sound events more accurately than the conventional method.