Impulsive audio events such as gunshots, explosions or glass shattering, are commonly associated with security threats, thus they are of particular interest for automated acoustic surveillance. Even though impulsive audio events are greatly influenced by their propagation path, little work has been done in multichannel detection, and most precedents available in the literature deal with single-channel detection systems. Unfortunately, the spatial dependence of impulsive sound recordings proves as a problem for robust performance under real conditions. It is possible, however, to take advantage of the spatial diversity provided by a wireless sensor network to counteract this problem. In this paper, we show how an ensemble of spatially diverse detectors can greatly improve the performance of the system. We propose an efficient multichannel detection system of impulsive audio events intended for low-cost Wireless Acoustic Sensor Networks. Our proposal is based on a low complexity classification algorithm and an efficient method to include temporal context into the feature vector. The obtained results show that the proposed detection system is capable of achieving a more than adequate performance without incurring large computational loads.

脉冲音频事件（例如枪声，爆炸或玻璃破碎）通常与安全威胁相关联，因此，它们对于自动声音监视尤为重要。即使脉冲音频事件受其传播路径影响很大，但在多通道检测中所做的工作很少，并且文献中的大多数先例都涉及单通道检测系统。不幸的是，脉冲录音的空间依赖性被证明是在真实条件下鲁棒性能的问题。但是，可以利用无线传感器网络提供的空间分集来解决此问题。在本文中，我们显示了一组空间上不同的检测器如何能够极大地改善系统的性能。我们提出了一种针对低成本无线声学传感器网络的脉冲音频事件的高效多通道检测系统。我们的建议基于低复杂度分类算法和一种将时间上下文包含到特征向量中的有效方法。所获得的结果表明，所提出的检测系统能够在不产生大量计算负荷的情况下实现足够的性能。