  This paper presents a comparative analysis on two artificial neural networks
(with different architectures) for the task of tempo estimation. For this
purpose, it also proposes the modeling, training and evaluation of a B-RNN
(Bidirectional Recurrent Neural Network) model capable of estimating tempo in
bpm (beats per minutes) of musical pieces, without using external auxiliary
modules. An extensive database (12,550 pieces in total) was curated to conduct
a quantitative and qualitative analysis over the experiment. Percussion-only
tracks were also included in the dataset. The performance of the B-RNN is
compared to that of state-of-the-art models. For further comparison, a
state-of-the-art CNN was also retrained with the same datasets used for the
B-RNN training. Evaluation results for each model and datasets are presented
and discussed, as well as observations and ideas for future research. Tempo
estimation was more accurate for the percussion only dataset, suggesting that
the estimation can be more accurate for percussion-only tracks, although
further experiments (with more of such datasets) should be made to gather
stronger evidence.
