Automatic Music Transcription has seen significant progress in recent years
by training custom deep neural networks on large datasets. However, these
models have required extensive domain-specific design of network architectures,
input/output representations, and complex decoding schemes. In this work, we
show that equivalent performance can be achieved using a generic
encoder-decoder Transformer with standard decoding methods. We demonstrate that
the model can learn to translate spectrogram inputs directly to MIDI-like
output events for several transcription tasks. This sequence-to-sequence
approach simplifies transcription by jointly modeling audio features and
language-like output dependencies, thus removing the need for task-specific
architectures. These results point toward possibilities for creating new Music
Information Retrieval models by focusing on dataset creation and labeling
rather than custom model design.

本文展示了使用通用 encoder-decoder Transformer 和标准解码方法可实现与专业领域特定设计模型同等效果的自动音乐转录方法，从而取消了任务特定架构的需求，简化了转录，为集中精力于数据集创建和标注而非模型设计提供了可能性。