Improved onset detection for traditional flute recordings using convolutional neural networks

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