This paper presents enhancements to the MT3 model, a state-of-the-art (SOTA)
token-based multi-instrument automatic music transcription (AMT) model. Despite
SOTA performance, MT3 has the issue of instrument leakage, where transcriptions
are fragmented across different instruments. To mitigate this, we propose
MR-MT3, with enhancements including a memory retention mechanism, prior token
sampling, and token shuffling are proposed. These methods are evaluated on the
Slakh2100 dataset, demonstrating improved onset F1 scores and reduced
instrument leakage. In addition to the conventional multi-instrument
transcription F1 score, new metrics such as the instrument leakage ratio and
the instrument detection F1 score are introduced for a more comprehensive
assessment of transcription quality. The study also explores the issue of
domain overfitting by evaluating MT3 on single-instrument monophonic datasets
such as ComMU and NSynth. The findings, along with the source code, are shared
to facilitate future work aimed at refining token-based multi-instrument AMT
models.

这篇论文介绍了 MT3 模型的改进，该模型是一种最先进的基于令牌的多乐器自动音乐转录模型。提出了 MR-MT3 来减轻该模型存在的乐器泄漏问题，并通过在 Slakh2100 数据集上进行评估，证明了改进方法的效果。研究还探讨了 MT3 在单乐器单声道数据集上的域过拟合问题，并分享了研究结果与源代码以促进未来研究修正基于令牌的多乐器自动音乐转录模型。

MR-MT3：记忆保留的多轨音乐转录以减轻乐器泄露

MR-MT3: Memory Retaining Multi-Track Music Transcription to Mitigate  Instrument Leakage

Recently proposed data collection frameworks for endangered language
documentation aim not only to collect speech in the language of interest, but
also to collect translations into a high-resource language that will render the
collected resource interpretable. We focus on this scenario and explore whether
we can improve transcription quality under these extremely low-resource
settings with the assistance of text translations. We present a neural
multi-source model and evaluate several variations of it on three low-resource
datasets. We find that our multi-source model with shared attention outperforms
the baselines, reducing transcription character error rate by up to 12.3%.

研究对濒危语言的数据采集，利用多源神经网络模型和翻译，成功提高了转录质量。