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

Citation Author(s):
Grigoris Bastas, Stefanos Koutoupis, Maximos Kaliakatsos-Papakostas, Vassilis Katsouros, Petros Maragos
Submitted by:
Grigoris Bastas
Last updated:
7 May 2022 - 5:56am
Document Type:
Poster
Document Year:
2022
Event:
Presenters Name:
Grigoris Bastas
Paper Code:
AUD-26.1

Abstract 

Abstract: 

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.

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bastas_icassp2022-poster.pdf

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