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Automatic Guitar Tablature Transcription from Audio Using Inharmonicity Regression and Bayesian Classification

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We propose two new methods to classify guitar strings for automated tablature transcription using only monophonic audio. The first method estimates the linear regression of log-inharmonicities of guitar strings with respect to their pitches and assigns unseen notes to the strings whose means and variances maximize the probability of their measured inharmonicities. The second method, developed as a baseline, characterizes the inharmonicity distribution of each fretboard position as a normal probability density, and then similarly assigns unseen notes to the fretboard positions that maximize the likelihood of their observed inharmonicities. Results from the standard Real World Corpus of guitar recordings show that exploiting regressions generally improves accuracy compared to our baseline, while both achieve adequate performance in guitar-independent test scenarios.

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