Onset Detection for String Instruments Using Bidirectional Temporal and Convolutional Recurrent Networks

Tomczak, Maciej and Hockman, Jason (2023) Onset Detection for String Instruments Using Bidirectional Temporal and Convolutional Recurrent Networks. In: 18th International Audio Mostly Conference, 30th August - 1st September 2023, Edinburgh, UK.

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Recent work in note onset detection has centered on deep learning models such as recurrent neural networks (RNN), convolutional neural networks (CNN) and more recently temporal convolutional networks (TCN), which achieve high evaluation accuracies for onsets characterized by clear, well-defined transients, as found in percussive instruments. However, onsets with less transient presence, as found in string instrument recordings, still pose a relatively difficult challenge for state-of-the-art algorithms. This challenge is further exacerbated by a paucity of string instrument data containing expert annotations. In this paper, we propose two new models for onset detection using bidirectional temporal and recurrent convolutional networks, which generalise to polyphonic signals and string instruments. We perform evaluations of the proposed methods alongside state-of-the-art algorithms for onset detection on a benchmark dataset from the MIR community, as well as on a test set from a newly proposed dataset of string instrument recordings with note onset annotations, comprising approximately 40 minutes and over 8,000 annotated onsets with varied expressive playing styles. The results demonstrate the effectiveness of both presented models, as they outperform the state-of-the-art algorithms on string recordings while maintaining comparative performance on other types of music.

Item Type: Conference or Workshop Item (Paper)
Identification Number: https://doi.org/10.1145/3616195.3616206
11 October 2023Accepted
11 October 2023Published Online
Uncontrolled Keywords: Music information retrieval, onset detection, recurrent convolutional neural networks, temporal convolutional networks
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH25 - design, and creative and performing arts > CAH25-02 - performing arts > CAH25-02-02 - music
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Gemma Tonks
Date Deposited: 17 Jan 2024 15:16
Last Modified: 17 Jan 2024 15:16
URI: https://www.open-access.bcu.ac.uk/id/eprint/15136

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