Exploring Procedural Data Generation for Automatic Acoustic Guitar Fingerpicking Transcription

• URL: http://arxiv.org/abs/2508.07987v1
• Date: Mon, 11 Aug 2025 13:52:17 GMT
• Title: Exploring Procedural Data Generation for Automatic Acoustic Guitar Fingerpicking Transcription
• Authors: Sebastian Murgul, Michael Heizmann,
• Abstract summary: This work investigates a procedural data generation pipeline as an alternative to real audio recordings for training transcription models.<n>Our approach synthesizes training data through four stages: knowledge-based fingerpicking tablature composition, MIDI performance rendering, physical modeling.<n>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.
• Score: 2.8544822698499255
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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