将节拍追踪模型适配于拉丁美洲音乐
Adapting Meter Tracking Models to Latin American Music
AI总结：
本文研究如何以少量标注数据和标准CPU训练即可将节拍与强拍追踪模型有效适配于两种拉丁美洲音乐传统，证明低成本适配在现实应用中可行。
摘要：
近年来，随着深度学习方法的引入，节拍与强拍追踪模型取得了显著改进。然而，尽管有这些改进，若干挑战依然存在。特别是，将现有模型适配于音乐信息检索（MIR）中代表性不足的音乐传统，通常意味着收集和标注大量数据，这既不切实际又耗时。迁移学习、数据增强和微调技术已在相关任务中得到相当成功的应用，并被认为能够缓解这一瓶颈。此外，在研究这些音乐传统时，并不要求模型泛化到多种主流音乐类型，而是需要在更受限、更同质的条件下表现良好。在这项工作中，我们研究了简单而有效的策略，将节拍与强拍追踪模型适配于两种不同的拉丁美洲音乐传统，并分析了这些适配在现实应用中关于数据和计算需求的可行性。与普遍看法相反，我们的研究结果表明，只需花费几分钟标注一部分数据，并在标准CPU机器上训练模型，就能取得良好性能，所需资源的精确数量取决于任务和数据集的复杂性。
英文摘要：
Beat and downbeat tracking models have improved significantly in recent years with the introduction of deep learning methods. However, despite these improvements, several challenges remain. Particularly, the adaptation of available models to underrepresented music traditions in MIR is usually synonymous with collecting and annotating large amounts of data, which is impractical and time-consuming. Transfer learning, data augmentation, and fine-tuning techniques have been used quite successfully in related tasks and are known to alleviate this bottleneck. Furthermore, when studying these music traditions, models are not required to generalize to multiple mainstream music genres but to perform well in more constrained, homogeneous conditions. In this work, we investigate simple yet effective strategies to adapt beat and downbeat tracking models to two different Latin American music traditions and analyze the feasibility of these adaptations in real-world applications concerning the data and computational requirements. Contrary to common belief, our findings show it is possible to achieve good performance by spending just a few minutes annotating a portion of the data and training a model in a standard CPU machine, with the precise amount of resources needed depending on the task and the complexity of the dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
