Fretiq: Browser-Native Electric Guitar String Classification via Engineered Spectral Features and Held-Out Free-Play Evaluation

Abstract:Identifying which string produces a given pitch in monophonic electric guitar audio is a fundamental classification challenge: a single pitch can often be produced on multiple strings at different fret positions, with timbral differences that prior listening studies confirm are largely imperceptible to untrained humans. Existing approaches using support vector machines and spectral envelope features have achieved F-measures of 0.90 for six-string electric guitar classification, while String-Inverse Frequency features in earlier work achieved F1 scores up to 0.72. We present Fretiq, a preliminary single-instrument, single-player browser-based string classification system built on a 26-dimensional feature representation integrating frequency band energies, spectral statistics, and 13 Mel-Frequency Cepstral Coefficients, achieving 97.1% shuffled frame-level validation accuracy across 322,215 balanced frames. An ablation study identifies MFCCs as the primary accuracy driver (92.2% to 97.1%). We additionally introduce Comparison Training -- a data collection methodology in which adjacent open-string and fifth-fret string pairs are recorded in deliberate alternation -- and evaluate its contribution via confusion matrix analysis. Comparison Training reduces the D3 to A2 frame-level confusion rate by 44% but shows mixed results on other targeted pairs. A held-out free-play evaluation on 103,000 frames yields 87.8% overall accuracy. We describe the feature extraction pipeline in both Python and TypeScript to guarantee training-inference parity, and document two critical implementation failure modes. The system runs entirely in-browser with no hexaphonic pickup, fretboard sensor, camera, or multi-microphone setup required.

Sound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)

arXiv:2607.18303 [cs.SD]

(or arXiv:2607.18303v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2607.18303 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.