Improved onset detection for traditional flute recordings using convolutional neural networks

Hockman, Jason and Ali-MacLachlan, Islah and Tomczak, Maciej and Southall, Carl (2017) Improved onset detection for traditional flute recordings using convolutional neural networks. In: The 7th International Workshop on Folk Music Analysis, 14-16 June 2017, Malaga.

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Abstract

The usage of ornaments is key attribute that defines the style of
a flute performances within the genre of Irish Traditional Music
(ITM). Automated analysis of ornaments in ITM would allow for
the musicological investigation of a player’s style and would be
a useful feature in the analysis of trends within large corpora of
ITM music. As ornament onsets are short and subtle variations
within an analysed signal, they are substantially more difficult to
detect than longer notes. This paper addresses the topic of onset
detection for notes, ornaments and breaths in ITM. We propose
a new onset detection method based on a convolutional neural
network (CNN) trained solely on flute recordings of ITM. The
presented method is evaluated alongside a state-of-the-art gen
eralised onset detection method using a corpus of 79 full-length
solo flute recordings. The results demonstrate that the proposed
system outperforms the generalised system over a range of musi
cal patterns idiomatic of the genre.

Item Type:	Conference or Workshop Item (Paper)
Dates:	Date Event 15 June 2017 Published 24 April 2017 Accepted
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Oana-Andreea Dumitrascu
Date Deposited:	28 Jun 2017 08:48
Last Modified:	22 Mar 2023 12:01
URI:	https://www.open-access.bcu.ac.uk/id/eprint/4748