Modeling Musical Onset Probabilities via Neural Distribution Learning

• URL: http://arxiv.org/abs/2002.03559v1
• Date: Mon, 10 Feb 2020 05:38:51 GMT
• Title: Modeling Musical Onset Probabilities via Neural Distribution Learning
• Authors: Jaesung Huh, Egil Martinsson, Adrian Kim, Jung-Woo Ha
• Abstract summary: Musical onset detection can be formulated as a time-to-event (TTE) or time-since-event (TSE) prediction task. We propose a novel method to model the probability of onsets by introducing a sequential density prediction model.
• Score: 11.094116617743962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Musical onset detection can be formulated as a time-to-event (TTE) or time-since-event (TSE) prediction task by defining music as a sequence of onset events. Here we propose a novel method to model the probability of onsets by introducing a sequential density prediction model. The proposed model estimates TTE & TSE distributions from mel-spectrograms using convolutional neural networks (CNNs) as a density predictor. We evaluate our model on the Bock dataset show-ing comparable results to previous deep-learning models.

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