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- W2952622869 abstract "The increasing accuracy of automatic chord estimation systems, the availability of vast amounts of heterogeneous reference annotations, and insights from annotator subjectivity research make chord label personalization increasingly important. Nevertheless, automatic chord estimation systems are historically exclusively trained and evaluated on a single reference annotation. We introduce a first approach to automatic chord label personalization by modeling subjectivity through deep learning of a harmonic interval-based chord label representation. After integrating these representations from multiple annotators, we can accurately personalize chord labels for individual annotators from a single model and the annotators' chord label vocabulary. Furthermore, we show that chord personalization using multiple reference annotations outperforms using a single reference annotation." @default.
- W2952622869 created "2019-06-27" @default.
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- W2952622869 date "2017-06-28" @default.
- W2952622869 modified "2023-09-25" @default.
- W2952622869 title "Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations" @default.
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- W2952622869 doi "https://doi.org/10.48550/arxiv.1706.09552" @default.
- W2952622869 hasPublicationYear "2017" @default.
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