首个直接从音频生成带指法和演奏技巧的吉他谱的端到端系统
TART: A Comprehensive Tool for Technique-Aware Audio-to-Tab Guitar Transcription
- 四阶段流程：先转音高，再识别演奏技巧，分配弦与品，最后生成谱面
- 在真实吉他录音上实现准确的指法匹配和滑音等技巧标注
- 适用于音乐制作人、吉他学习者，尤其适合处理复杂演奏片段
自动音乐转录（AMT）在钢琴领域已取得显著进展，但吉他转录仍受限于若干关键挑战。现有系统无法检测并标注表达性演奏技巧（如滑音、弯音、击弦），且常将音符错误映射至错误的弦与品组合。此外，以往模型多基于小规模孤立数据集训练，泛化能力差。为此，我们提出一个四阶段端到端流程，可直接从音频生成详细吉他谱。系统包括：(1) 通过适配吉他数据集的钢琴转录模型完成音频到MIDI音高的转换；(2) 使用MLP进行表达性技巧分类；(3) 基于Transformer的弦与品分配；(4) 基于LSTM的谱面生成。据我们所知，该框架是首个能从吉他音频中生成含准确指法与表达性标签的详细谱面的系统。
原文摘要 · Abstract (English)
Automatic Music Transcription (AMT) has advanced significantly for the piano, but transcription for the guitar remains limited due to several key challenges. Existing systems fail to detect and annotate expressive techniques (e.g., slides, bends, percussive hits) and incorrectly map notes to the wrong string and fret combination in the generated tablature. Furthermore, prior models are typically trained on small, isolated datasets, limiting their generalizability to real-world guitar recordings. To overcome these limitations, we propose a four-stage end-to-end pipeline that produces detailed guitar tablature directly from audio. Our system consists of (1) Audio-to-MIDI pitch conversion through a piano transcription model adapted to guitar datasets; (2) MLP-based expressive technique classification; (3) Transformer-based string and fret assignment; and (4) LSTM-based tablature generation. To the best of our knowledge, this framework is the first to generate detailed tablature with accurate fingerings and expressive labels from guitar audio.
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