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TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

Paper recorded by Signals 4 on 2026-09-10 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-10 on arXiv · recorded by Signals 4 on 2026-09-11

Category: cs.LG · 机器学习 · first seen 2026-09-11

Abstract

Automatic Music Transcription (AMT) for guitar remains limited by three challenges: existing systems often fail to capture expressive techniques such as slides, bends, and percussive hits; they often assign notes to incorrect string-fret combinations; and they are typically trained on clean recordings, limiting their generalization to noisy real-world audio. To address these challenges, we propose

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#69 most recent of 215 cs.LG papers we have recorded · ↑ newer: From Protocols to Evidence: Bounded Claims for AI in Service of the Co · ↓ older: CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
Cite this page: TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription: the #69 most recent of 215 cs.LG papers we have recorded (as of 2026-09-10). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/tart-a-modular-tool-for-technique-aware-audio-to-tablature-guitar-transcription.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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