A multi-channel fusion framework for audio event detection
A multi-channel fusion framework for audio event detection
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DOI：
10.1109/waspaa.2015.7336889
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Huy Phan;M. Maass;L. Hertel;Radoslaw Mazur;A. Mertins
中科院分区：
文献类型：
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作者：
Huy Phan;M. Maass;L. Hertel;Radoslaw Mazur;A. Mertins
在本文中，我们提出了一个简单的，但有效的多通道融合框架联合声事件检测和分类。在各个通道上的联合问题被视为一个回归问题，以估计事件的起始和偏移位置。作为一个中间结果，我们还获得了后验概率，它测量事件发生和偏移存在于时间位置的置信度。它通过累积不同通道的后验概率来促进融合问题。然后基于求和后验概率确定检测假设。虽然所提出的融合框架似乎是简单和自然的，它显着优于ITC-Irst数据库上的所有单通道基线系统。我们还表明，一个接一个地添加到融合系统的通道产生的性能改善，融合系统的性能总是优于那些单独的通道同行。
We propose in this paper a simple, yet efficient multi-channel fusion framework for joint acoustic event detection and classification. The joint problem on individual channels is posed as a regression problem to estimate event onset and offset positions. As an intermediate result, we also obtain the posterior probabilities which measure the confidence that event onsets and offsets are present at a temporal position. It facilitates the fusion problem by accumulating the posterior probabilities of different channels. The detection hypotheses are then determined based on the summed posterior probabilities. While the proposed fusion framework appears to be simple and natural, it significantly outperforms all the single-channel baseline systems on the ITC-Irst database. We also show that adding channels one by one into the fusion system yields performance improvements, and the performance of the fusion system is always better than those of the individual-channel counterparts.