Commercial adoption of automatic music composition requires the capability of
generating diverse and high-quality music suitable for the desired context
(e.g., music for romantic movies, action games, restaurants, etc.). In this
paper, we introduce combinatorial music generation, a new task to create
varying background music based on given conditions. Combinatorial music
generation creates short samples of music with rich musical metadata, and
combines them to produce a complete music. In addition, we introduce ComMU, the
first symbolic music dataset consisting of short music samples and their
corresponding 12 musical metadata for combinatorial music generation. Notable
properties of ComMU are that (1) dataset is manually constructed by
professional composers with an objective guideline that induces regularity, and
(2) it has 12 musical metadata that embraces composers' intentions. Our results
show that we can generate diverse high-quality music only with metadata, and
that our unique metadata such as track-role and extended chord quality improves
the capacity of the automatic composition. We highly recommend watching our
video before reading the paper (this https URL).

该研究介绍了组合音乐生成和符号音乐数据集，该方法能够仅通过音乐元数据自动组合出多样的高质量音乐，并通过追踪角色和扩展和弦质量等元数据来提高其自动生成音乐的能力。

ComMU: 组合音乐生成数据集

ComMU: Dataset for Combinatorial Music Generation

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。

DadaGP: 用于序列模型的分词 GuitarPro 歌曲数据集

DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models

Symbolic music datasets are important for music information retrieval and
musical analysis. However, there is a lack of large-scale symbolic datasets for
classical piano music. In this article, we create a GiantMIDI-Piano (GP)
dataset containing 38,700,838 transcribed notes and 10,855 unique solo piano
works composed by 2,786 composers. We extract the names of music works and the
names of composers from the International Music Score Library Project (IMSLP).
We search and download their corresponding audio recordings from the internet.
We further create a curated subset containing 7,236 works composed by 1,787
composers by constraining the titles of downloaded audio recordings containing
the surnames of composers. We apply a convolutional neural network to detect
solo piano works. Then, we transcribe those solo piano recordings into Musical
Instrument Digital Interface (MIDI) files using a high-resolution piano
transcription system. Each transcribed MIDI file contains the onset, offset,
pitch, and velocity attributes of piano notes and pedals. GiantMIDI-Piano
includes 90% live performance MIDI files and 10\% sequence input MIDI files. We
analyse the statistics of GiantMIDI-Piano and show pitch class, interval,
trichord, and tetrachord frequencies of six composers from different eras to
show that GiantMIDI-Piano can be used for musical analysis. We evaluate the
quality of GiantMIDI-Piano in terms of solo piano detection F1 scores, metadata
accuracy, and transcription error rates. We release the source code for
acquiring the GiantMIDI-Piano dataset at
this https URL

本文创建了一个具有 38700838 个音符和 10855 个独奏钢琴作品的 GiantMIDI-Piano dataset，使用卷积神经网络探测独奏钢琴作品，并使用高分辨率钢琴转录系统将这些作品转录为 MIDI 文件，可用于音乐信息检索和音乐分析。