论文标题
探索变压器在自动钢琴转录上的潜力
Exploring Transformer's potential on automatic piano transcription
论文作者
论文摘要
关于自动音乐转录（AMT）的最新研究使用卷积神经网络和经常性神经网络来对从音乐信号到符号符号的映射进行建模。基于高分辨率钢琴转录系统，我们探讨了合并另一个强大的序列变换工具-Transformer-以处理AMT问题的可能性。我们认为变压器的性质使其更适合某些AMT子任务。我们通过在Maestro数据集上的实验和地图数据集上的跨数据库评估来确认变压器对速度检测任务的优越性。引入变压器网络后，我们观察到帧级和注释级指标的性能改进。
Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic notation. Based on a high-resolution piano transcription system, we explore the possibility of incorporating another powerful sequence transformation tool -- the Transformer -- to deal with the AMT problem. We argue that the properties of the Transformer make it more suitable for certain AMT subtasks. We confirm the Transformer's superiority on the velocity detection task by experiments on the MAESTRO dataset and a cross-dataset evaluation on the MAPS dataset. We observe a performance improvement on both frame-level and note-level metrics after introducing the Transformer network.
