Automatic Music Transcription (AMT) is a crucial technology in music
information processing. Despite recent improvements in performance through
machine learning approaches, existing methods often achieve high accuracy in
domains with abundant annotation data, primarily due to the difficulty of
creating annotation data. A practical transcription model requires an
architecture that does not require an annotation data. In this paper, we
propose an annotation-free transcription model achieved through the utilization
of scalable synthetic audio for pre-training and adversarial domain confusion
using unannotated real audio. Through evaluation experiments, we confirm that
our proposed method can achieve higher accuracy under annotation-free
conditions compared to when learning with mixture of annotated real audio data.
Additionally, through ablation studies, we gain insights into the scalability
of this approach and the challenges that lie ahead in the field of AMT
research.

提出了一种无需注释数据的音乐自动转录模型，通过利用可扩展的合成音频进行预训练和对抗性域混淆，实现了比使用混合注释真实音频数据学习时更高的准确性，在 AMT 研究领域中，揭示了这种方法的可扩展性和面临的挑战。

无注释自动音乐转录：基于可扩展合成数据和对抗领域混淆技术

Annotation-Free Automatic Music Transcription with Scalable Synthetic  Data and Adversarial Domain Confusion

The goal of semantic parsing is to map natural language to a machine
interpretable meaning representation language (MRL). One of the constraints
that limits full exploration of deep learning technologies for semantic parsing
is the lack of sufficient annotation training data. In this paper, we propose
using sequence-to-sequence in a multi-task setup for semantic parsing with a
focus on transfer learning. We explore three multi-task architectures for
sequence-to-sequence modeling and compare their performance with an
independently trained model. Our experiments show that the multi-task setup
aids transfer learning from an auxiliary task with large labeled data to a
target task with smaller labeled data. We see absolute accuracy gains ranging
from 1.0% to 4.4% in our in- house data set, and we also see good gains ranging
from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and
semantic auxiliary tasks.

本文提出了一种基于多任务框架和序列到序列模型的语义解析方法，旨在解决语料数据不足限制，较少标注数据任务可以通过从大量标注数据任务中传递学习的方式得到提升，实验结果在自己的数据集中获得了 1.0% 到 4.4% 的准确度提升，在 ATIS 语义解析任务中，准确度提升了 2.5% 到 7.0%。