STAR Drums: A Dataset for Automatic Drum Transcription

Abstract

Current state-of-the-art Automatic Drum Transcription (ADT) algorithms make use of neural networks. To train such models, large amounts of annotated data are needed. We introduce the Separate-Tracks-Annotate-Resynthesize Drums (STAR Drums) dataset, derived from full audio recordings that include mixtures of drum instruments, melodic instruments, and vocals. First, we separate the music recordings into a drum stem and a non-drum stem by applying a Music Source Separation (MSS) algorithm, and then automatically annotate the drum stem with an ADT algorithm. The annotations are utilized for the re-synthesis of the drum stem using samplebased virtual drum instruments. Finally, we mix the re-synthesized drum stem with the original non-drum stem to obtain the final mix. In summary, STAR Drums comprises annotated synthesized drum sounds mixed with real recordings of melodic instruments and vocals, offering several benefits: high temporal accuracy of annotations; training data that includes recordings of instruments played by musicians, rather than solely relying on MIDI-rendered audio; a large number of supported drum classes; the possibility to customize the final mix by, for instance, applying additional processing to the drum stem, as both drum and non-drum stems are provided; and suitable licenses of audio files for making the dataset fully available to the research community. We demonstrate that, in the context of ADT, training with STAR Drums achieves superior performance compared to training with datasets solely relying on MIDI-rendered data and that the synthesized nature of the drum stem does not diminish performance.

If you make use of the dataset, please cite the accompanying paper which provides a detailed description: Weber, P., Uhle, C., Müller, M. and Lang, M. (2025) ‘STAR Drums: A Dataset for Automatic Drum Transcription’, Transactions of the International Society for Music Information Retrieval, 8(1), p. 248–264.

Download

For easier file handling, we divided the zipped dataset into 30 GB chunks. To rejoin the parts, use:

cat STAR_Drums_full.zip.part-* > STAR_Drums_full.zip

Then, you can verify the integrity of the created zip file. Save STAR_Drums_full.zip.sha256 in the same directory where STAR_Drums_full.zip is located and run:

sha256sum -c STAR_Drums_full.zip.sha256

This will take a few minutes and should then return ./STAR_Drums_full.zip: OK.

Structure

Annotations for 18 drum classes, audio stems, and MIDI files used to render the re-synthesized drum stems are located in the ./data directory, organized into sub-folders for the training, validation, and test splits.

Each annotation consist of a time stamp in seconds, followed by the drum class, followed by the MIDI velocity value

Scripts: In ./data/scripts, we provide

code to create mix files for the MUSDB-18 items whose licenses do not permit direct distribution.

code as a starting point for creating data-augmented versions of the mixes by pre-processing the re-synthesized drum stems or altering mixing gains.

the midi_mappings which were used to create MIDI files from the annotations using the virtual instruments referred to in the paper

摘要

当前最先进的自动鼓点转录（Automatic Drum Transcription, ADT）算法均采用神经网络。训练此类模型需要大量带标注的训练数据。本文提出分离-标注-再合成鼓组数据集（Separate-Tracks-Annotate-Resynthesize Drums, STAR Drums），该数据集源自包含鼓组乐器、旋律乐器与人声混合的完整音频录音。首先，我们通过音乐源分离（Music Source Separation, MSS）算法将音乐录音分离为鼓声部与非鼓声部，随后利用ADT算法自动为鼓声部添加标注。上述标注将用于基于采样的虚拟鼓乐器对鼓声部进行再合成。最后，我们将再合成后的鼓声部与原始非鼓声部混合，得到最终混音作品。

简而言之，STAR Drums数据集由带标注的合成鼓组声音与真实旋律乐器、人声录音混合而成，具备多项优势：标注具备极高的时间精度；训练数据包含乐手演奏的乐器录音，而非仅依赖MIDI渲染音频；支持的鼓类数量众多；用户可自定义最终混音效果，例如对鼓声部进行额外处理，因为数据集同时提供鼓声部与非鼓声部分轨；音频文件采用适配的授权协议，可向研究社区完全开放该数据集。我们验证了，在自动鼓点转录任务中，使用STAR Drums数据集进行训练的性能优于仅依赖MIDI渲染数据的数据集，且鼓声部的合成属性并未降低模型性能。

### 引用说明
若使用本数据集，请引用配套的详细描述论文：Weber, P., Uhle, C., Müller, M. and Lang, M. (2025) 《STAR Drums：面向自动鼓点转录的数据集》，《国际音乐信息检索协会汇刊》（Transactions of the International Society for Music Information Retrieval），8(1)，第248-264页。

### 下载
为便于文件处理，我们将压缩后的数据集拆分为30 GB的分卷。如需重新合并分卷，请执行以下命令：

cat STAR_Drums_full.zip.part-* > STAR_Drums_full.zip

随后可验证生成的ZIP文件完整性：将STAR_Drums_full.zip.sha256保存至与STAR_Drums_full.zip相同的目录下，并运行：

sha256sum -c STAR_Drums_full.zip.sha256

该过程将耗时数分钟，完成后应返回 `./STAR_Drums_full.zip: OK`。

### 数据集结构
./data目录下包含18类鼓组的标注、音频分轨以及用于渲染再合成鼓声部的MIDI文件，并按训练集、验证集与测试集划分为子文件夹。

每条标注包含以秒为单位的时间戳、鼓类名称以及MIDI力度值。

### 配套脚本
在./data/scripts目录下，我们提供以下代码：
1. 用于为MUSDB-18中许可证不允许直接分发的条目创建混音文件的代码；
2. 作为数据增强版本混音的起点代码，可通过预处理再合成的鼓声部或调整混音增益实现；
3. 本研究所用虚拟乐器对应的MIDI映射表，用于从标注生成MIDI文件。