构建首个大规模吉他音频与六线谱配对数据集，支持多样演奏风格研究。
GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures
- 采集5.9小时真实电吉他录音，搭配六线谱标注。
- 通过音箱增广生成29.5小时音色变体，扩展数据多样性。
- 可训练合成、转录、演奏技法识别等新模型，适合音乐信息检索研究者。
近年来，由于吉他演奏技法多样、音色复杂，其在音乐信息检索（MIR）领域受到越来越多关注。但深度学习进展受限于数据稀缺与标注不足。为此，我们提出吉他音频与六线谱数据集GOAT，包含5.9小时高质量电吉他直接输入录音，涵盖多种吉他与演奏者。同时提出一种基于吉他音箱的数据增强策略，实现近无限音色变化，提供初始29.5小时增强音频。每段录音均以六线谱标注，使用Guitar Pro格式及文本化标记编码，支持弦号、品位号与多种演奏技巧。基于GOAT，我们取得具有竞争力的MIDI转录结果，并展示了一种新型自动六线谱转录方法的初步成果。我们希望GOAT能推动吉他相关MIR任务的新模型研发，涵盖音色合成、乐谱转录与演奏技法检测等方向。
原文摘要 · Abstract (English)
In recent years, the guitar has received increased attention from the music information retrieval (MIR) community driven by the challenges posed by its diverse playing techniques and sonic characteristics. Mainly fueled by deep learning approaches, progress has been limited by the scarcity and limited annotations of datasets. To address this, we present the Guitar On Audio and Tablatures (GOAT) dataset, comprising 5.9 hours of unique high-quality direct input audio recordings of electric guitars from a variety of different guitars and players. We also present an effective data augmentation strategy using guitar amplifiers which delivers near-unlimited tonal variety, of which we provide a starting 29.5 hours of audio. Each recording is annotated using guitar tablatures, a guitar-specific symbolic format supporting string and fret numbers, as well as numerous playing techniques. For this we utilise both the Guitar Pro format, a software for tablature playback and editing, and a text-like token encoding. Furthermore, we present competitive results using GOAT for MIDI transcription and preliminary results for a novel approach to automatic guitar tablature transcription. We hope that GOAT opens up the possibilities to train novel models on a wide variety of guitar-related MIR tasks, from synthesis to transcription to playing technique detection.
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