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

Michelson J, Stern R, Sullivan T. Automatic Guitar Tablature Transcription from Audio Using Inharmonicity Regression and Bayesian Classification. In: AES Convention 145. Audio Engineering Society; 2018.pp. 10091:1-. Available from: https://aes.org/publications/elibrary-page/?id=19817

@inproceedings{Michelson2018_19817,
  author = {Michelson, Jonathan and Stern, Richard and Sullivan, Thomas},
  title = {{Automatic Guitar Tablature Transcription from Audio Using Inharmonicity Regression and Bayesian Classification}},
  booktitle = {AES Convention 145},
  pages = {10091:1-},
  year = {2018},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=19817}
}

TY  - CPAPER
TI  - Automatic Guitar Tablature Transcription from Audio Using Inharmonicity Regression and Bayesian Classification
AU  - Michelson, Jonathan
AU  - Stern, Richard
AU  - Sullivan, Thomas
T2  - AES Convention 145
SP  - 1
M1  - Paper 10091
PY  - 2018
DA  - 2018/10/06
UR  - https://aes.org/publications/elibrary-page/?id=19817
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -