Algorithms for automatic piano transcription have improved dramatically in
recent years due to new datasets and modeling techniques. Recent developments
have focused primarily on adapting new neural network architectures, such as
the Transformer and Perceiver, in order to yield more accurate systems. In this
work, we study transcription systems from the perspective of their training
data. By measuring their performance on out-of-distribution annotated piano
data, we show how these models can severely overfit to acoustic properties of
the training data. We create a new set of audio for the MAESTRO dataset,
captured automatically in a professional studio recording environment via
Yamaha Disklavier playback. Using various data augmentation techniques when
training with the original and re-performed versions of the MAESTRO dataset, we
achieve state-of-the-art note-onset accuracy of 88.4 F1-score on the MAPS
dataset, without seeing any of its training data. We subsequently analyze these
data augmentation techniques in a series of ablation studies to better
understand their influence on the resulting models.

通过研究训练数据的角度，本文通过在 MAESTRO 数据集的原始和重新执行版本上使用各种数据增强技术，获得了 MAPS 数据集的最新音符起始准确性，而无需查看其他的训练数据。

基于数据驱动的鲁棒自动钢琴转录分析

A Data-Driven Analysis of Robust Automatic Piano Transcription

Taking long-term spectral and temporal dependencies into account is essential
for automatic piano transcription. This is especially helpful when determining
the precise onset and offset for each note in the polyphonic piano content. In
this case, we may rely on the capability of self-attention mechanism in
Transformers to capture these long-term dependencies in the frequency and time
axes. In this work, we propose hFT-Transformer, which is an automatic music
transcription method that uses a two-level hierarchical frequency-time
Transformer architecture. The first hierarchy includes a convolutional block in
the time axis, a Transformer encoder in the frequency axis, and a Transformer
decoder that converts the dimension in the frequency axis. The output is then
fed into the second hierarchy which consists of another Transformer encoder in
the time axis. We evaluated our method with the widely used MAPS and MAESTRO
v3.0.0 datasets, and it demonstrated state-of-the-art performance on all the
F1-scores of the metrics among Frame, Note, Note with Offset, and Note with
Offset and Velocity estimations.

提出了一种名为 hFT-Transformer 的自动音乐转录方法，该方法利用了两种级别的分层频率 - 时间 Transformer 结构，能够捕捉音频中的长期依赖关系，并在评估中表现出最先进的性能。

分层频时间转换的自动钢琴转录

Automatic Piano Transcription with Hierarchical Frequency-Time  Transformer

We present an automatic piano transcription system that converts polyphonic
audio recordings into musical scores. This has been a long-standing problem of
music information processing, and recent studies have made remarkable progress
in the two main component techniques: multipitch detection and rhythm
quantization. Given this situation, we study a method integrating
deep-neural-network-based multipitch detection and statistical-model-based
rhythm quantization. In the first part, we conducted systematic evaluations and
found that while the present method achieved high transcription accuracies at
the note level, some global characteristics of music, such as tempo scale,
metre (time signature), and bar line positions, were often incorrectly
estimated. In the second part, we formulated non-local statistics of pitch and
rhythmic contents that are derived from musical knowledge and studied their
effects in inferring those global characteristics. We found that these
statistics are markedly effective for improving the transcription results and
that their optimal combination includes statistics obtained from separated hand
parts. The integrated method had an overall transcription error rate of 7.1%
and a downbeat F-measure of 85.6% on a dataset of popular piano music, and the
generated transcriptions can be partially used for music performance and
assisting human transcribers, thus demonstrating the potential for practical
applications.

提出了一种自动钢琴转录系统，该系统利用基于深度神经网络的多音高检测和基于统计模型的节奏量化相结合的方法提高了音乐信息处理的准确性，在系统性评估中发现一些全局特征误差较大，而由音乐知识推导的音高和节奏内容的非局部统计显著提高了转录结果的准确性。