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
