A Few-Sample Strategy for Guitar Tablature Transcription Based on Inharmonicity Analysis and Playability Constraints

The prominent strategical approaches regarding the problem of guitar tablature transcription rely either on fingering patterns encoding or on the extraction of string-related audio features. The current work combines the two aforementioned strategies in an explicit manner by employing two discrete c...

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Bibliographic Details
Published inICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 771 - 775
Main Authors Bastas, Grigoris, Koutoupis, Stefanos, Kaliakatsos-Papakostas, Maximos, Katsouros, Vassilis, Maragos, Petros
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.05.2022
Subjects
Adaptation models
Conferences
Encoding
Feature extraction
few-sample strategy
genetic algorithm
inharmonicity
Prediction algorithms
Recording
Signal processing algorithms
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Summary:The prominent strategical approaches regarding the problem of guitar tablature transcription rely either on fingering patterns encoding or on the extraction of string-related audio features. The current work combines the two aforementioned strategies in an explicit manner by employing two discrete components for string-fret classification. It extends older few-sample modeling strategies by introducing various adaptation schemes for the first stage of audio processing, taking advantage of the inharmonic characteristics of guitar sound. Physical limitations and common standards of human performers are incorporated in a genetic algorithm which constitutes a second contextual-based module that further processes the initial audio-based predictions. The proposed methods are evaluated on both annotated guitar performances and isolated note recordings.
ISSN:2379-190X
DOI:10.1109/ICASSP43922.2022.9747169