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- W4302305827 abstract "Version identification (VI) has seen substantial progress over the past few years. On the one hand, the introduction of the metric learning paradigm has favored the emergence of scalable yet accurate VI systems. On the other hand, using features focusing on specific aspects of musical pieces, such as melody, harmony, or lyrics, yielded interpretable and promising performances. In this work, we build upon these recent advances and propose a metric learning-based system systematically leveraging four dimensions commonly admitted to convey musical similarity between versions: melodic line, harmonic structure, rhythmic patterns, and lyrics. We describe our deliberately simple model architecture, and we show in particular that an approximated representation of the lyrics is an efficient proxy to discriminate between versions and non-versions. We then describe how these features complement each other and yield new state-of-the-art performances on two publicly available datasets. We finally suggest that a VI system using a combination of melodic, harmonic, rhythmic and lyrics features could theoretically reach the optimal performances obtainable on these datasets." @default.
- W4302305827 created "2022-10-06" @default.
- W4302305827 creator A5069902254 @default.
- W4302305827 creator A5082487751 @default.
- W4302305827 date "2022-10-03" @default.
- W4302305827 modified "2023-09-27" @default.
- W4302305827 title "And what if two musical versions don't share melody, harmony, rhythm, or lyrics ?" @default.
- W4302305827 doi "https://doi.org/10.48550/arxiv.2210.01256" @default.
- W4302305827 hasPublicationYear "2022" @default.
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