Improving the Generation of Ground Truths Based on Partially Ordered Lists

نویسندگان

  • Julián Urbano
  • Mónica Marrero
  • Diego Martín
  • Juan Llorens Morillo
چکیده

Ground truths based on partially ordered lists have been used for some years now to evaluate the effectiveness of Music Information Retrieval systems, especially in tasks related to symbolic melodic similarity. However, there has been practically no meta-evaluation to measure or improve the correctness of these evaluations. In this paper we revise the methodology used to generate these ground truths and disclose some issues that need to be addressed. In particular, we focus on the arrangement and aggregation of the relevant results, and show that it is not possible to ensure lists completely consistent. We develop a measure of consistency based on Average Dynamic Recall and propose several alternatives to arrange the lists, all of which prove to be more consistent than the original method. The results of the MIREX 2005 evaluation are revisited using these alternative ground truths.

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تاریخ انتشار 2010