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TENT: Technique-Embedded Note Tracking for Real-World Guitar Solo Recordings.

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Bibliographic Details
Title:	TENT: Technique-Embedded Note Tracking for Real-World Guitar Solo Recordings.
Authors:	Ting-Wei Su, Yuan-Ping Chen, Li Su, Yi-Hsuan Yang
Source:	International Society for Music Information Retrieval Conference Proceedings; 2019, p15-28, 14p
Subject Terms:	MUSICAL analysis, GUITAR playing, SIGNAL processing, MUSICAL performance
Abstract:	The employment of playing techniques such as string bend and vibrato in electric guitar performance makes it difficult to transcribe the note events using general note tracking methods. These methods analyze the contour of fundamental frequency computed from a given audio signal, but they do not consider the variation in the contour caused by the playing techniques. To address this issue, we present a model called technique-embedded note tracking (TENT) that uses the result of playing technique detection to inform note event estimation. We evaluate the proposed model on a dataset of 42 unaccompanied lead guitar phrases. Our experiments showed that TENT can nicely recognize complicated skills in monophonic guitar solos and improve the F-score of note event estimation by 14.7% compared to an existing method. For reproducibility, we share the Python source code of our implementation of TENT at the following GitHub repo: https://github.com/srviest/SoloLa. [ABSTRACT FROM AUTHOR]
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Database:	Complementary Index

Description
Abstract:	The employment of playing techniques such as string bend and vibrato in electric guitar performance makes it difficult to transcribe the note events using general note tracking methods. These methods analyze the contour of fundamental frequency computed from a given audio signal, but they do not consider the variation in the contour caused by the playing techniques. To address this issue, we present a model called technique-embedded note tracking (TENT) that uses the result of playing technique detection to inform note event estimation. We evaluate the proposed model on a dataset of 42 unaccompanied lead guitar phrases. Our experiments showed that TENT can nicely recognize complicated skills in monophonic guitar solos and improve the F-score of note event estimation by 14.7% compared to an existing method. For reproducibility, we share the Python source code of our implementation of TENT at the following GitHub repo: https://github.com/srviest/SoloLa. [ABSTRACT FROM AUTHOR]
DOI:	10.5334/tismir.23