Proc. of the 21st Int. Society for Music Information Retrieval Conf., Montréal, Canada, 2020
Accurate and flexible representations of music data are paramount to addressing MIR tasks, yet many of the existing approaches are difficult to interpret or rigid in nature. This work introduces two new song representations for structure-based retrieval methods: Surface Pattern Preservation (SuPP), a continuous song representation, and Matrix Pattern Preservation (MaPP), SuPP’s discrete counterpart. These representations come equipped with several user-defined parameters so that they are adaptable for a range of MIR tasks. Experimental results show MaPP as successful in addressing the cover song task on a set of Mazurka scores, with a mean precision of 0.965 and recall of 0.776. SuPP and MaPP also show promise in other MIR applications, such as novel-segment detection and genre classification, the latter of which demonstrates their suitability as inputs for machine learning problems.
Claire Savard, Erin H. Bugbee, Melissa R. McGuirl, Katherine M. Kinnaird, “SuPP & MaPP: Adaptable Structure-Based Representations for MIR Tasks”, in Proc. of the 21st Int. Society for Music Information Retrieval Conf., Montréal, Canada, 2020.
© Claire Savard, Erin H. Bugbee, Melissa R. McGuirl, Katherine M. Kinnaird. L
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