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Evaluating Hierarchical Structure in Music Annotations.

Brian McFee1,2, Oriol Nieto3, Morwaread M Farbood2

  • 1Center for Data Science, New York UniversityNew York, NY, United States.

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Summary
This summary is machine-generated.

This study introduces a new metric to evaluate hierarchical music structure annotations, revealing how musical features and time scales influence listener agreement and improving music cognition research.

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evaluationhierarchyinter-annotator agreementmusic structure

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Area of Science:

  • Music Informatics Research (MIR)
  • Music Cognition
  • Computational Musicology

Background:

  • Music structure exists at multiple scales, leading to varied listener interpretations.
  • Existing methods for evaluating music structure annotations do not adequately handle hierarchical data.
  • Understanding inter-annotator agreement is crucial for music cognition and MIR.

Purpose of the Study:

  • To develop and generalize an evaluation metric for hierarchical music structure annotations.
  • To investigate inter-annotator agreement on multilevel music annotations.
  • To analyze the influence of acoustic properties and genre on hierarchical annotations.

Main Methods:

  • Derived a novel evaluation metric for comparing hierarchical music annotations across multiple levels.
  • Applied the metric to analyze inter-annotator agreement on two music corpora.
  • Investigated the relationship between acoustic features and hierarchical annotations.

Main Results:

  • The new metric allows for holistic comparison of hierarchical annotations.
  • Inter-annotator agreement varies based on musical features, time scale, and genre.
  • The study provides insights into the perceptual differences in music structure perception.

Conclusions:

  • The developed metric advances the evaluation of hierarchical music structure.
  • Findings contribute to a better understanding of music cognition and perception.
  • This work enables more accurate evaluation of music segmentation algorithms.