A Cross-Version Approach to Audio Representation Learning for Orchestral Music
Krause M, Weiß C, Müller M (2023)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2023
Publisher: Imprint
Pages Range: 832-839
Conference Proceedings Title: Proceedings of the International Society for Music Information Retrieval Conference (ISMIR)
Event location: Mailand
Abstract
Deep learning systems have become popular for tackling a variety of music information retrieval tasks. However, these systems often require large amounts of labeled data for supervised training, which can be very costly to obtain. To alleviate this problem, recent papers on learning music audio representations employ alternative training strategies that utilize unannotated data. In this paper, we introduce a novel cross-version approach to audio representation learning that can be used with music datasets containing several versions (performances) of a musical work. Our method exploits the correspondences that exist between two versions of the same musical section. We evaluate our proposed cross-version approach qualitatively and quantitatively on complex orchestral music recordings and show that it can better capture aspects of instrumentation compared to techniques that do not use cross-version information.
Authors with CRIS profile
Michael Krause
International Audio Laboratories Erlangen (AudioLabs)
Christof Weiß
International Audio Laboratories Erlangen (AudioLabs)
Meinard Müller
Lehrstuhl für Semantische Audiosignalverarbeitung (AudioLabs)
How to cite
APA:
Krause, M., Weiß, C., & Müller, M. (2023). A Cross-Version Approach to Audio Representation Learning for Orchestral Music. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) (pp. 832-839). Mailand, IT: Imprint.
MLA:
Krause, Michael, Christof Weiß, and Meinard Müller. "A Cross-Version Approach to Audio Representation Learning for Orchestral Music." Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Mailand Imprint, 2023. 832-839.
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