  In this paper, we propose an efficient and reproducible deep learning model
for musical onset detection (MOD). We first review the state-of-the-art deep
learning models for MOD, and identify their shortcomings and challenges: (i)
the lack of hyper-parameter tuning details, (ii) the non-availability of code
for training models on other datasets, and (iii) ignoring the network
capability when comparing different architectures. Taking the above issues into
account, we experiment with seven deep learning architectures. The most
efficient one achieves equivalent performance to our implementation of the
state-of-the-art architecture. However, it has only 28.3% of the total number
of trainable parameters compared to the state-of-the-art. Our experiments are
conducted using two different datasets: one mainly consists of instrumental
music excerpts, and another developed by ourselves includes only solo singing
voice excerpts. Further, inter-dataset transfer learning experiments are
conducted. The results show that the model pre-trained on one dataset fails to
detect onsets on another dataset, which denotes the importance of providing the
implementation code to enable re-training the model for a different dataset.
Datasets, code and a Jupyter notebook running on Google Colab are publicly
available to make this research understandable and easy to reproduce.
