We present a method for the generation of Midi files of piano music. The
method models the right and left hands using two networks, where the left hand
is conditioned on the right hand. This way, the melody is generated before the
harmony. The Midi is represented in a way that is invariant to the musical
scale, and the melody is represented, for the purpose of conditioning the
harmony, by the content of each bar, viewed as a chord. Finally, notes are
added randomly, based on this chord representation, in order to enrich the
generated audio. Our experiments show a significant improvement over the state
of the art for training on such datasets, and demonstrate the contribution of
each of the novel components.

本文提出了一种基于神经网络的方法，用于生成钢琴音乐的 Midi 文件。该方法使用两个网络分别生成左右手的音符，其中左手取决于右手。Midi 文件以一种对音乐音阶不变的方式呈现，旨在通过将每个小节视为和弦的内容来表示旋律，从而调节和弦。最后，根据这个和弦表示，随机添加音符，以丰富生成的音频。实验表明，该方法在训练过程中比现有技术取得了显著的改进，并展示了每个新颖组件的贡献。

A-Muze-Net：基于生成的旋律编排和声进行音乐生成

A-Muze-Net: Music Generation by Composing the Harmony based on the  Generated Melody

While there are many music datasets with emotion labels in the literature,
they cannot be used for research on symbolic-domain music analysis or
generation, as there are usually audio files only. In this paper, we present
the EMOPIA (pronounced `yee-m\`{o}-pi-uh') dataset, a shared multi-modal (audio
and MIDI) database focusing on perceived emotion in pop piano music, to
facilitate research on various tasks related to music emotion. The dataset
contains 1,087 music clips from 387 songs and clip-level emotion labels
annotated by four dedicated annotators. Since the clips are not restricted to
one clip per song, they can also be used for song-level analysis. We present
the methodology for building the dataset, covering the song list curation, clip
selection, and emotion annotation processes. Moreover, we prototype use cases
on clip-level music emotion classification and emotion-based symbolic music
generation by training and evaluating corresponding models using the dataset.
The result demonstrates the potential of EMOPIA for being used in future
exploration on piano emotion-related MIR tasks.

本文介绍了 EMOPIA 数据集 —— 一个音频和 MIDI 综合的多模态数据库，着重于流行钢琴音乐中的情感。数据集包含 1087 个音乐片段，涵盖了 387 首歌曲，具有 4 个专业标注员标注的片段级情感标签。在该数据集上，我们提出了音乐情感分类和基于情感的音乐生成模型，并通过模型训练和评估证明了 EMOPIA 在未来的研究中具有很高的潜力。

EMOPIA：一种用于情感识别和基于情感音乐生成的多模态流行钢琴数据集

EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and  Emotion-based Music Generation

Realistic music generation is a challenging task. When building generative
models of music that are learnt from data, typically high-level representations
such as scores or MIDI are used that abstract away the idiosyncrasies of a
particular performance. But these nuances are very important for our perception
of musicality and realism, so in this work we embark on modelling music in the
raw audio domain. It has been shown that autoregressive models excel at
generating raw audio waveforms of speech, but when applied to music, we find
them biased towards capturing local signal structure at the expense of
modelling long-range correlations. This is problematic because music exhibits
structure at many different timescales. In this work, we explore autoregressive
discrete autoencoders (ADAs) as a means to enable autoregressive models to
capture long-range correlations in waveforms. We find that they allow us to
unconditionally generate piano music directly in the raw audio domain, which
shows stylistic consistency across tens of seconds.

本文探讨了采用自回归离散自编码器（ADAs）来模拟音乐块之间的长期依赖关系的方法，实现了在原始音频域中生成具有风格一致性的钢琴音乐。