Automatic music transcription (AMT) has achieved high accuracy for piano due
to the availability of large, high-quality datasets such as MAESTRO and MAPS,
but comparable datasets are not yet available for other instruments. In recent
work, however, it has been demonstrated that aligning scores to transcription
model activations can produce high quality AMT training data for instruments
other than piano. Focusing on the guitar, we refine this approach to training
on score data using a dataset of commercially available score-audio pairs. We
propose the use of a high-resolution piano transcription model to train a new
guitar transcription model. The resulting model obtains state-of-the-art
transcription results on GuitarSet in a zero-shot context, improving on
previously published methods.

使用高分辨率钢琴转录模型训练新的吉他转录模型，从而在零样本情况下在 GuitarSet 上获得最先进的转录结果，改进了之前发表的方法。

高分辨率吉他转录通过领域适应

High Resolution Guitar Transcription via Domain Adaptation

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

Automatic Music Transcription (AMT), inferring musical notes from raw audio,
is a challenging task at the core of music understanding. Unlike Automatic
Speech Recognition (ASR), which typically focuses on the words of a single
speaker, AMT often requires transcribing multiple instruments simultaneously,
all while preserving fine-scale pitch and timing information. Further, many AMT
datasets are "low-resource", as even expert musicians find music transcription
difficult and time-consuming. Thus, prior work has focused on task-specific
architectures, tailored to the individual instruments of each task. In this
work, motivated by the promising results of sequence-to-sequence transfer
learning for low-resource Natural Language Processing (NLP), we demonstrate
that a general-purpose Transformer model can perform multi-task AMT, jointly
transcribing arbitrary combinations of musical instruments across several
transcription datasets. We show this unified training framework achieves
high-quality transcription results across a range of datasets, dramatically
improving performance for low-resource instruments (such as guitar), while
preserving strong performance for abundant instruments (such as piano).
Finally, by expanding the scope of AMT, we expose the need for more consistent
evaluation metrics and better dataset alignment, and provide a strong baseline
for this new direction of multi-task AMT.

本文利用序列到序列传递学习的多任务自然语言处理模型，在多个不同的乐器转录数据集上对多种音乐乐器进行了联合转录，展示了其在低资源乐器的表现结果显著，同时保持了高质量的表现结果，是多任务自动音乐转录领域的强有力基线。