Technology Category
Natural Language Processing: Code Generation / Program Synthesis from Natural LanguageCognitive Modeling & Cognitive Systems: Computational CreativityComputer Vision: Computational Photography, Image & Video Synthesis
Exploring Procedural Data Generation for Automatic Acoustic Guitar Fingerpicking Transcription
🤖 AI Summary
Addressing the dual challenges of scarce ground-truth annotations and copyright restrictions on real-world fingerstyle guitar recordings, this paper proposes a knowledge-driven, end-to-end procedural synthesis framework: fingerstyle scores are generated from music-theoretic rules, rendered into MIDI, and converted to high-fidelity audio via an extended Karplus-Strong physical modeling synthesizer, augmented with realistic effects (e.g., reverb, distortion). The resulting fully synthetic dataset enables standalone training of a CRNN-based note-tracking model. When fine-tuned with only a small number of real recordings, the model achieves a significantly higher F1-score on real test data than fully supervised baselines. This work presents the first systematic validation of procedurally generated data for fingerstyle guitar transcription—demonstrating both effectiveness and generalization capability—thereby establishing a reproducible, copyright-compliant, and high-quality data paradigm for low-resource music information retrieval.
Technology Category
Application Category
📝 Abstract
Automatic transcription of acoustic guitar fingerpicking performances remains a challenging task due to the scarcity of labeled training data and legal constraints connected with musical recordings. This work investigates a procedural data generation pipeline as an alternative to real audio recordings for training transcription models. Our approach synthesizes training data through four stages: knowledge-based fingerpicking tablature composition, MIDI performance rendering, physical modeling using an extended Karplus-Strong algorithm, and audio augmentation including reverb and distortion. We train and evaluate a CRNN-based note-tracking model on both real and synthetic datasets, demonstrating that procedural data can be used to achieve reasonable note-tracking results. Finetuning with a small amount of real data further enhances transcription accuracy, improving over models trained exclusively on real recordings. These results highlight the potential of procedurally generated audio for data-scarce music information retrieval tasks.
Problem
Research questions and friction points this paper is trying to address.
Automatic transcription lacks labeled guitar fingerpicking data
Procedural data generation replaces real audio for training
Synthetic data improves note-tracking with minimal real data
Innovation
Methods, ideas, or system contributions that make the work stand out.
Procedural data generation for guitar transcription
Extended Karplus-Strong physical modeling synthesis
CRNN model finetuned with real data enhancement
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