ONLINE REAL-TIME ONSET DETECTION WITH RECURRENT NEURAL NETWORKS
Sebastian Böck, Andreas Arzt, Florian Krebs, Markus Schedl · 2012
We present a new onset detection algorithm which operates online in real time without delay. Our method incorporates a recurrent neural network to model the sequence of onsets based solely on causal audio signal information. Comparative performance against existing state-of-the-art online and offline algorithms was evaluated using a very large database. The new method ‐ despite being an online algorithm ‐ shows performance only slightly short of the best existing offline methods while outperforming standard approaches.