论文标题
MIDI退化工具包：符号音乐的增强和更正
The MIDI Degradation Toolkit: Symbolic Music Augmentation and Correction
论文作者
论文摘要
在本文中，我们介绍了MIDI降解工具包（MDTK），其中包含功能，这些功能是输入音乐摘录（带有音高，发作时间和持续时间的一组音符），并返回该摘录的“降级”版本，并带有某些错误（或错误）。使用该工具包，我们创建了更改和损坏的MIDI摘录数据集1.0版（ACME V1.0），并提出了增加难以检测，分类，定位，定位和更正降解的四个任务。我们假设为这些任务培训的模型在（例如）如果应用于后处理步骤，可以改善自动音乐转录性能。为此，MDTK包括一个脚本，该脚本可以测量转录中不同类型错误的分布，并创建具有相似属性的退化数据集。 MDTK的降解也可以在训练期间动态应用于数据集（有或没有上述脚本），从而生成新的降级摘录。 MDTK也可以用来测试任何用于将MIDI（或类似）数据作为输入（例如设计用于语音分离，度量对准或和弦检测的系统）到此类转录错误或其他嘈杂数据的系统的鲁棒性。该工具包和数据集都在线公开可用，我们鼓励社区的贡献和反馈。
In this paper, we introduce the MIDI Degradation Toolkit (MDTK), containing functions which take as input a musical excerpt (a set of notes with pitch, onset time, and duration), and return a "degraded" version of that excerpt with some error (or errors) introduced. Using the toolkit, we create the Altered and Corrupted MIDI Excerpts dataset version 1.0 (ACME v1.0), and propose four tasks of increasing difficulty to detect, classify, locate, and correct the degradations. We hypothesize that models trained for these tasks can be useful in (for example) improving automatic music transcription performance if applied as a post-processing step. To that end, MDTK includes a script that measures the distribution of different types of errors in a transcription, and creates a degraded dataset with similar properties. MDTK's degradations can also be applied dynamically to a dataset during training (with or without the above script), generating novel degraded excerpts each epoch. MDTK could also be used to test the robustness of any system designed to take MIDI (or similar) data as input (e.g. systems designed for voice separation, metrical alignment, or chord detection) to such transcription errors or otherwise noisy data. The toolkit and dataset are both publicly available online, and we encourage contribution and feedback from the community.
