Enhanced Peak Picking for Onset Detection with Recurrent Neural Networks

Description

We present a new neural network based peak-picking algorithm for common onset detection functions. Compared to existing handcrafted 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 significant improvement over existing methods in cases of signals with previously unknown levels.

Period	25 Sept 2013
Event title	6th International Workshop on Machine Learning and Music, European Conference on Machine Learning (ECML 2013)
Event type	Conference
Location	Czech RepublicShow on map