Combining Gestural and Audio Approaches to the Classification of Violin Bow Strokes

Wilson, William and Ali-MacLachlan, Islah and Granieri, Niccolo (2022) Combining Gestural and Audio Approaches to the Classification of Violin Bow Strokes. In: 10th International Workshop on Folk Music Analysis, 10th - 14th June 2022, Sheffield, UK.

Text (Conference Paper)
FMA2022_Wilson et al.pdf - Published Version

Download (472kB)


This paper details a brief exploration of methods by which gestural and audio based approaches may be used in the classification of violin performances. These are based upon a multimodal dataset. Onsets are derived from audio signals and used to segment synchronous gestural recordings, allowing for the classification of individual bow strokes utilising data of either type—or both. Classification accuracies for the purposes of participant identification ranged between 71.06% and 91.35% for various data type combinations. Classification accuracies for the identification of bowing technique were typically lower, ranging between 53.33% and 77.35%. The findings of this paper inform a number of recommendations for future work. These are to be considered in the development of a principally similar dataset, for the analysis of traditional fiddle playing styles.

Item Type: Conference or Workshop Item (Paper)
1 April 2022Accepted
14 June 2022Published
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Islah Ali-Maclachlan
Date Deposited: 07 Dec 2022 15:08
Last Modified: 22 Mar 2023 12:00

Actions (login required)

View Item View Item


In this section...