论文标题
一种数据驱动的方法，用于考虑基于深度学习的吉他小型转录系统的可行性和成对的可能性
A Data-Driven Methodology for Considering Feasibility and Pairwise Likelihood in Deep Learning Based Guitar Tablature Transcription Systems
论文作者
论文摘要
吉他的调整转录是音乐信息检索领域内的一个重要但正在研究的问题。传统的信号处理方法仅在任务上提供有限的性能，而且几乎没有用于培训机器学习模型的转录标签的声学数据。但是，仅凭吉他转录标签就以小组的形式广泛使用，这通常在网上吉他手之间共享。在这项工作中，利用了一系列象征性的小组来估计吉他上笔记的成对可能性。重新制定了基线调整转录模型的输出层，因此可以纳入抑制损失，以阻止不太可能注意对的共激活。这自然会针对吉他实施可玩性限制，并产生与估计成对似然的符号数据更一致的曲线。通过这种方法，我们表明符号库可以用于塑造表转录模型的预测的分布，即使很少有声数据。
Guitar tablature transcription is an important but understudied problem within the field of music information retrieval. Traditional signal processing approaches offer only limited performance on the task, and there is little acoustic data with transcription labels for training machine learning models. However, guitar transcription labels alone are more widely available in the form of tablature, which is commonly shared among guitarists online. In this work, a collection of symbolic tablature is leveraged to estimate the pairwise likelihood of notes on the guitar. The output layer of a baseline tablature transcription model is reformulated, such that an inhibition loss can be incorporated to discourage the co-activation of unlikely note pairs. This naturally enforces playability constraints for guitar, and yields tablature which is more consistent with the symbolic data used to estimate pairwise likelihoods. With this methodology, we show that symbolic tablature can be used to shape the distribution of a tablature transcription model's predictions, even when little acoustic data is available.
