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Musical onset detection can be formulated as a time-to-event (TTE) ortime-since-event (TSE) prediction task by defining music as a sequence of onsetevents. Here we propose a novel method to model the probability of onsets byintroducing a sequential density prediction model. The proposed model estimatesTTE & TSE distributions from mel-spectrograms using convolutional neuralnetworks (CNNs) as a density predictor. We evaluate our model on the Bockdataset show-ing comparable results to previous deep-learning models.

Modeling Musical Onset Probabilities via Neural Distribution Learning