Technology Category
Planning, Routing, and Scheduling: Planning with Language ModelsNatural Language Processing: Code Generation / Program Synthesis from Natural LanguageConstraint Satisfaction and Optimization: Constraint Learning and Acquisition
Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription
🤖 AI Summary
This work addresses the lack of guitar-specific physical constraints—such as string-fret mapping and playability—in MIDI symbolic music. We propose the first T5-based method for automatic transcription from MIDI to guitar tablature, framing the task as conditional sequence-to-sequence translation. Our approach innovatively jointly models string-fret ambiguity resolution and physical playability constraints, while incorporating tunings and capo configurations as controllable conditions and employing context-sensitive decoding. We introduce a customized tokenization scheme, train on fused multi-source datasets (DadaGP, GuitarToday, Leduc), and design a dual-axis evaluation metric combining fingering accuracy and playability. Experiments demonstrate significant improvements over A*-search baselines and commercial tools (e.g., Guitar Pro) across all metrics. The method supports arbitrary tunings and capo positions, generating tablatures that are both highly accurate and practically playable.
Technology Category
Application Category
📝 Abstract
Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges, including string-fret ambiguity and physical playability. The proposed system leverages diverse datasets, including DadaGP, GuitarToday, and Leduc, with novel data pre-processing and tokenization strategies. We have developed metrics for tablature accuracy and playability to quantitatively evaluate the performance. The experimental results demonstrate that the Fretting-Transformer surpasses baseline methods like A* and commercial applications like Guitar Pro. The integration of context-sensitive processing and tuning/capo conditioning further enhances the model's performance, laying a robust foundation for future developments in automated guitar transcription.
Problem
Research questions and friction points this paper is trying to address.
Transcribing MIDI to guitar tablature accurately
Resolving string-fret ambiguity in transcription
Ensuring physical playability of transcribed tablature
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses T5 transformer for MIDI to tablature
Novel data pre-processing and tokenization
Integrates context-sensitive and tuning conditioning
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