Enhanced peak picking for onset detection with recurrent neural networks
Enhanced peak picking for onset detection with recurrent neural networks
复制标题
使用循环神经网络增强峰值选取以进行起始检测
DOI：
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发表时间：
2013
期刊：
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通讯作者：
G. Widmer
中科院分区：
文献类型：
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作者：
G. Widmer
提出了一种新的基于神经网络的取峰算法——一种用于常见的起始检测函数的算法。与现有的手工方法相比，它具有更好的性能，并且导致假阴性检测的数量大大减少。性能是基于超过25k个带注释的发作的庞大数据集进行评估的，并且在先前未知水平的信号情况下，与现有方法相比，显示出显着的改进。
We present a new neural network based peak-picking algo- rithm for common onset detection functions. Compared to existing hand- crafted methods it yields a better performance and leads to a much lower number of false negative detections. The performance is evaluated on basis of a huge dataset with over 25k annotated onsets and shows a signicant improvement over existing methods in cases of signals with previously unknown levels.