Krause M, Müller M (2023)
Publication Type: Journal article
Publication year: 2023
Pages Range: 1-12
DOI: 10.1109/TASLP.2023.3291506
Instrument activity detection is a fundamental task in music information retrieval, serving as a basis for many applications, such as music recommendation, music tagging, or remixing. Most published works on this task cover popular music and music for smaller ensembles. In this paper, we embrace orchestral and opera music recordings as a rarely considered scenario for automated instrument activity detection. Orchestral music is particularly challenging since it consists of intricate polyphonic and polytimbral sound mixtures where multiple instruments are playing simultaneously. Orchestral instruments can naturally be arranged in hierarchical taxonomies, according to instrument families. As the main contribution of this paper, we show that a hierarchical classification approach can be used to detect instrument activity in our scenario, even if only few fine-grained, instrument-level annotations are available. We further consider additional loss terms for improving the hierarchical consistency of predictions. For our experiments, we collect a dataset containing 14 hours of orchestral music recordings with aligned instrument activity annotations. Finally, we perform an analysis of the behavior of our proposed approach with regard to potential confounding errors.
APA:
Krause, M., & Müller, M. (2023). Hierarchical Classification for Instrument Activity Detection in Orchestral Music Recordings. IEEE/ACM Transactions on Audio, Speech and Language Processing, 1-12. https://doi.org/10.1109/TASLP.2023.3291506
MLA:
Krause, Michael, and Meinard Müller. "Hierarchical Classification for Instrument Activity Detection in Orchestral Music Recordings." IEEE/ACM Transactions on Audio, Speech and Language Processing (2023): 1-12.
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