Automatic music transcription (AMT) is the task of transcribing audio
recordings into symbolic representations. Recently, neural network-based
methods have been applied to AMT, and have achieved state-of-the-art results.
However, many previous systems only detect the onset and offset of notes
frame-wise, so the transcription resolution is limited to the frame hop size.
There is a lack of research on using different strategies to encode onset and
offset targets for training. In addition, previous AMT systems are sensitive to
the misaligned onset and offset labels of audio recordings. Furthermore, there
are limited researches on sustain pedal transcription on large-scale datasets.
In this article, we propose a high-resolution AMT system trained by regressing
precise onset and offset times of piano notes. At inference, we propose an
algorithm to analytically calculate the precise onset and offset times of piano
notes and pedal events. We show that our AMT system is robust to the misaligned
onset and offset labels compared to previous systems. Our proposed system
achieves an onset F1 of 96.72% on the MAESTRO dataset, outperforming previous
onsets and frames system of 94.80%. Our system achieves a pedal onset F1 score
of 91.86\%, which is the first benchmark result on the MAESTRO dataset. We have
released the source code and checkpoints of our work at
this https URL

本文提出了一种高分辨率的基于神经网络的自动音乐转录系统，可以精确定位钢琴音符的起始和终止时间，并且具有良好的鲁棒性，对于 MAESTRO 数据集的 onset F1 分数达到了 96.72％，对于 pedal onset F1 分数更是取得了 91.86％的好成绩。

通过回归按键起始和终止时间进行高分辨率钢琴转录

High-resolution Piano Transcription with Pedals by Regressing Onset and  Offset Times

In this study, we train deep neural networks to classify composer on a
symbolic domain. The model takes a two-channel two-dimensional input, i.e.,
onset and note activations of time-pitch representation, which is converted
from MIDI recordings and performs a single-label classification. On the
experiments conducted on MAESTRO dataset, we report an F1 value of 0.8333 for
the classification of 13~classical composers.

该研究使用深度神经网络训练分类器，能够根据 MIDI 音频记录的时间和音符活动，对古典作曲家进行分类，对 MAESTRO 数据集进行实验，得到 13 个古典作曲家分类的 F1 值为 0.8333。

符号表示下的深度作曲家分类

Deep Composer Classification Using Symbolic Representation

We consider the problem of learning high-level controls over the global
structure of generated sequences, particularly in the context of symbolic music
generation with complex language models. In this work, we present the
Transformer autoencoder, which aggregates encodings of the input data across
time to obtain a global representation of style from a given performance. We
show it is possible to combine this global representation with other temporally
distributed embeddings, enabling improved control over the separate aspects of
performance style and melody. Empirically, we demonstrate the effectiveness of
our method on various music generation tasks on the MAESTRO dataset and a
YouTube dataset with 10,000+ hours of piano performances, where we achieve
improvements in terms of log-likelihood and mean listening scores as compared
to baselines.

本篇论文介绍 Transformer 自编码器用于符号音乐生成，提高性能风格和旋律的分开控制能力，并在 MAESTRO 及 Youtube 数据集上实现了比基线更好的结果。

使用 Transformer 自动编码器对音乐风格进行编码

Encoding Musical Style with Transformer Autoencoders

Generating musical audio directly with neural networks is notoriously
difficult because it requires coherently modeling structure at many different
timescales. Fortunately, most music is also highly structured and can be
represented as discrete note events played on musical instruments. Herein, we
show that by using notes as an intermediate representation, we can train a
suite of models capable of transcribing, composing, and synthesizing audio
waveforms with coherent musical structure on timescales spanning six orders of
magnitude (~0.1 ms to ~100 s), a process we call Wave2Midi2Wave. This large
advance in the state of the art is enabled by our release of the new MAESTRO
(MIDI and Audio Edited for Synchronous TRacks and Organization) dataset,
composed of over 172 hours of virtuosic piano performances captured with fine
alignment (~3 ms) between note labels and audio waveforms. The networks and the
dataset together present a promising approach toward creating new expressive
and interpretable neural models of music.

该研究使用 MAESTRO 数据集中的音符事件作为中间表示，训练了一系列的神经网络模型，用于转录、合成和生成具备连贯乐曲结构的音频波形，从而实现了跨六个数量级（0.1ms 到 100s）的音频合成。