Universal Onset Detection with Bidirectional Long Short-Term Memory Neural Networks

Many different onset detection methods have been proposed in recent years. However those that perform well tend to be highly specialised for certain types of music, while those that are more widely applicable give only moderate performance. In this paper we present a new onset detector with superior performance and temporal precision for all kinds of music, including complex music mixes. It is based on auditory spectral features and relative spectral differences processed by a bidirectional Long Short-Term Memory recurrent neural network, which acts as reduction function. The network is trained with a large database of onset data covering various genres and onset types. Due to the data driven nature, our approach does not require the onset detection method and its parameters to be tuned to a particular type of music. We compare results on the Bello onset data set and can conclude that our approach is on par with related results on the same set and outperforms them in most cases in terms of F1-measure. For complex music with mixed onset types, an absolute improvement of 3.6% is reported.