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.

中文翻译：

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通过神经网络估计音乐节奏——比较分析

本文对用于速度估计任务的两个人工神经网络（具有不同架构）进行了比较分析。为此，它还提出了 B-RNN（双向循环神经网络）模型的建模、训练和评估，该模型能够在不使用外部辅助模块的情况下估计音乐作品的 bpm（每分钟节拍）速度。策划了一个广泛的数据库（总共 12,550 个）以对实验进行定量和定性分析。仅打击乐曲目也包含在数据集中。B-RNN 的性能与最先进模型的性能进行了比较。为了进一步比较，还使用用于 B-RNN 训练的相同数据集重新训练了最先进的 CNN。展示和讨论了每个模型和数据集的评估结果，以及对未来研究的观察和想法。对于仅打击乐的数据集，速度估计更准确，这表明对于仅打击乐的音轨，估计可以更准确，尽管应该进行进一步的实验（使用更多此类数据集）以收集更有力的证据。