Enhanced peak picking for onset detection with recurrent neural networks

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.