Efficient Convolutional Neural Network For Audio Event Detection
Efficient Convolutional Neural Network For Audio Event Detection
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发表时间：
2017-09
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通讯作者：
Matthias Meyer;Lukas Cavigelli;L. Thiele
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作者：
Matthias Meyer;Lukas Cavigelli;L. Thiele
传感器网络、物联网和网络物理系统中使用的无线分布式系统对资源效率提出了很高的要求。在网络边缘对数据进行高级预处理和分类有助于减少通信需求并减少需要集中处理的数据量。在分布式声学传感领域，高分类率算法与资源受限嵌入式系统的结合至关重要。不幸的是，声学事件检测算法具有较高的内存和计算需求，并且不适合在网络边缘执行。本文通过对用于音频事件检测的卷积神经网络应用结构优化来解决这些问题，将内存需求减少 500 倍以上，将计算量减少 2.1 倍，同时性能提高 9.2%。
Wireless distributed systems as used in sensor networks, Internet-of-Things and cyber-physical systems, impose high requirements on resource efficiency. Advanced preprocessing and classification of data at the network edge can help to decrease the communication demand and to reduce the amount of data to be processed centrally. In the area of distributed acoustic sensing, the combination of algorithms with a high classification rate and resource-constraint embedded systems is essential. Unfortunately, algorithms for acoustic event detection have a high memory and computational demand and are not suited for execution at the network edge. This paper addresses these aspects by applying structural optimizations to a convolutional neural network for audio event detection to reduce the memory requirement by a factor of more than 500 and the computational effort by a factor of 2.1 while performing 9.2% better.