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.
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Text (Conference Paper)
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Abstract
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) | ||||||
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| 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 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/13990 |
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