The majority of recent progress in Optical Music Recognition (OMR) has been
achieved with Deep Learning methods, especially models following the end-to-end
paradigm, reading input images and producing a linear sequence of tokens.
Unfortunately, many music scores, especially piano music, cannot be easily
converted to a linear sequence. This has led OMR researchers to use custom
linearized encodings, instead of broadly accepted structured formats for music
notation. Their diversity makes it difficult to compare the performance of OMR
systems directly. To bring recent OMR model progress closer to useful results:
(a) We define a sequential format called Linearized MusicXML, allowing to train
an end-to-end model directly and maintaining close cohesion and compatibility
with the industry-standard MusicXML format. (b) We create a dev and test set
for benchmarking typeset OMR with MusicXML ground truth based on the OpenScore
Lieder corpus. They contain 1,438 and 1,493 pianoform systems, each with an
image from IMSLP. (c) We train and fine-tune an end-to-end model to serve as a
baseline on the dataset and employ the TEDn metric to evaluate the model. We
also test our model against the recently published synthetic pianoform dataset
GrandStaff and surpass the state-of-the-art results.

使用端到端模型和 Linearized MusicXML 格式进行 Optical Music Recognition (OMR)，并通过与已发布的数据集进行评估和对比，超过了目前的最佳结果。

钢琴乐谱的实用端到端光学音乐识别

Practical End-to-End Optical Music Recognition for Pianoform Music

Originating in the Renaissance and burgeoning in the digital era, tablatures
are a commonly used music notation system which provides explicit
representations of instrument fingerings rather than pitches. GuitarPro has
established itself as a widely used tablature format and software enabling
musicians to edit and share songs for musical practice, learning, and
composition. In this work, we present DadaGP, a new symbolic music dataset
comprising 26,181 song scores in the GuitarPro format covering 739 musical
genres, along with an accompanying tokenized format well-suited for generative
sequence models such as the Transformer. The tokenized format is inspired by
event-based MIDI encodings, often used in symbolic music generation models. The
dataset is released with an encoder/decoder which converts GuitarPro files to
tokens and back. We present results of a use case in which DadaGP is used to
train a Transformer-based model to generate new songs in GuitarPro format. We
discuss other relevant use cases for the dataset (guitar-bass transcription,
music style transfer and artist/genre classification) as well as ethical
implications. DadaGP opens up the possibility to train GuitarPro score
generators, fine-tune models on custom data, create new styles of music,
AI-powered songwriting apps, and human-AI improvisation.

本文章介绍了 DadaGP，这是一个全新的符号音乐数据集，包括 26,181 首 GuitarPro 格式的歌曲，涵盖 739 种音乐流派，以及一个适用于生成序列模型（如 Transformer）的令牌格式。使用 DadaGP 可以训练吉他谱生成器、创建新的音乐风格、音乐风格转移和艺术家 / 流派分类等用例。